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@book{abbott_2015,
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@incollection{abdi_2003,
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@article{abdi_2010,
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@article{ablin_2019,
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  month = may,
  journal = {arXiv:1805.10054 [cs, stat]},
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  primaryclass = {cs, stat},
  abstract = {Independent component analysis (ICA) is a widespread data exploration technique, where observed signals are modeled as linear mixtures of independent components. From a machine learning point of view, it amounts to a matrix factorization problem with a statistical independence criterion. Infomax is one of the most used ICA algorithms. It is based on a loss function which is a non-convex loglikelihood. We develop a new majorizationminimization framework adapted to this loss function. We derive an online algorithm for the streaming setting, and an incremental algorithm for the finite sum setting, with the following benefits. First, unlike most algorithms found in the literature, the proposed methods do not rely on any critical hyperparameter like a step size, nor do they require a line-search technique. Second, the algorithm for the finite sum setting, although stochastic, guarantees a decrease of the loss function at each iteration. Experiments demonstrate progress on the state-of-the-art for large scale datasets, without the necessity for any manual parameter tuning.},
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@article{acunzo_2012,
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@article{aczel_2020,
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@book{adler_1981,
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  year = {1981},
  series = {Classics in {{Applied Mathematics}}},
  publisher = {{Society for Industrial and Applied Mathematics}},
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  abstract = {This book deals primarily with the sample function behaviour of Gaussian, and related, random fields; i.e. stochastic processes whose arguments vary in a continuous fashion over some subset of  RN{$\mathscr{R}$}N{$<$}math display="inline" overflow="scroll"{$>$} {$<$}mrow{$>$} {$<$}msup{$><$}mrow{$><$}mi{$>\mathscr{R}<$}/mi{$><$}/mrow{$><$}mrow{$><$}mi{$>$}N{$<$}/mi{$><$}/mrow{$><$}/msup{$>$} {$<$}/mrow{$>$} {$<$}/math{$>$} , N-dimensional Euclidean space. The problems that arise in describing this behaviour in the multiparameter setting are qualitatively different to those covered by the one-dimensional theory. Indeed, most of the really interesting problems are of a geometrical nature, and actually disappear in the simple case. The purpose of this book is to collect within one cover most of the contents of the substantial literature devoted to the sample function analysis of random fields, including a reasonably full and self-contained account of the geometry needed for its understanding. While working on this book I tried to write with two readers in mind. One was a mathematician, for whom it was necessary to present what I feel is a rather beautiful theory, incorporating the standards of rigour and exactness that mathematicians are accustomed to. The other reader was an applied scientist, working in one of the many subjects that employs random fields as an element of mathematical modelling. His needs are quite different, for he requires a full statement of useful results, presented in such a way that he is not required to follow long mathematical proofs in order to understand and use them. It is possible that by attempting to please both readers I will have satisfied neither. Nevertheless, I beg the patience of both readers when they come upon parts of the text obviously written for the other. To be able to read the book from cover to cover a reader will need a good working knowledge (at graduate student level) of the basic features of modern probability theory and, for Chapter 8, measure theory. Although a brief review of those aspects of probability theory required is given in Chapter 1, it is unreasonable to assume that this can serve as anything other than a convenient reference for a reader already familiar with this material. However, with the applied scientist in mind, I have attempted to present the main results of the book in such a fashion that they can be understood and used by a reader whose knowledge of probability incorporates little more than the notions of expectation and stochastic convergence.},
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@article{adler_2000,
  title = {On Excursion Sets, Tube Formulas and Maxima of Random Fields},
  author = {Adler, Robert J.},
  year = {2000},
  month = feb,
  journal = {The Annals of Applied Probability},
  volume = {10},
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  doi = {10.1214/aoap/1019737664},
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@book{adler_2007,
  title = {Random Fields and Geometry},
  author = {Adler, Robert J. and Taylor, Jonathan E.},
  year = {2007},
  series = {Springer Monographs in Mathematics},
  number = {115},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-48112-8 978-0-387-48116-6},
  langid = {english},
  lccn = {QA274.45 .A345 2007},
  keywords = {Global differential geometry,Random fields},
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@book{adler_2011,
  title = {Topological Complexity of Smooth Random Functions: {{\'Ecole}} d'{{\'Et\'e}} de {{Probabilit\'es}} de {{Saint-Flour XXXIX}} - 2009},
  shorttitle = {Topological Complexity of Smooth Random Functions},
  author = {Adler, Robert J. and Taylor, Jonathan E.},
  year = {2011},
  series = {Lecture Notes in Mathematics},
  number = {2019},
  publisher = {{Springer}},
  address = {{Heidelberg ; New York}},
  isbn = {978-3-642-19579-2},
  langid = {english},
  lccn = {QA274.4 .A435 2011},
  keywords = {Congresses,Gaussian processes,Random fields},
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}

@article{adler_2017,
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  author = {Adler, Robert J. and Bartz, Kevin and Kou, Sam C. and Monod, Anthea},
  year = {2017},
  month = apr,
  journal = {arXiv:1704.08562 [math, stat]},
  eprint = {1704.08562},
  eprinttype = {arxiv},
  primaryclass = {math, stat},
  abstract = {We introduce Lipschitz-Killing curvature (LKC) regression, a new method to produce \$(1-\textbackslash alpha)\$ thresholds for signal detection in random fields that does not require knowledge of the spatial correlation structure. The idea is to fit observed empirical Euler characteristics to the Gaussian kinematic formula via generalized least squares, which quickly and easily provides statistical estimates of the LKCs --- complex topological quantities that can be extremely challenging to compute, both theoretically and numerically. With these estimates, we can then make use of a powerful parametric approximation via Euler characteristics for Gaussian random fields to generate accurate \$(1-\textbackslash alpha)\$ thresholds and \$p\$-values. The main features of our proposed LKC regression method are easy implementation, conceptual simplicity, and facilitated diagnostics, which we demonstrate in a variety of simulations and applications.},
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  keywords = {60G60; 62G32; 62E20,Mathematics - Statistics Theory},
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@article{adler_Inprep.,
  title = {Applications of Random Fields and Geometry},
  author = {Adler, Robert J and Taylor, Jonathan E and Worsley, Keith J},
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@article{ahmed_2008,
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@book{aldous_1989,
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  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1802324115},
  abstract = {Reproducibility has been one of the major tools science has used to help establish the validity and importance of scientific findings since Philosophical Transactions of the Royal Society was established in 1665 (1). Since that time the process of discovery has evolved to make use of new technologies and methods in a changing regulatory and social environment. The Sackler Colloquium ``Reproducibility of Research: Issues and Proposed Remedies,'' which took place on March 8\textendash 10, 2017, convened a wide range of research community stakeholders to address our understanding of transparency and reproducibility in the modern research context with two related questions: what does reproducibility mean in different research contexts, and what remedies increase reproducibility and transparency? We approach the topic of reproducibility with sensitivity to its complexity, spanning a wide range of issues from data collection and reporting to communication of scientific findings by scientists and nonscientists alike. The Colloquium was organized by David Allison, Richard Shiffrin, Victoria Stodden, and Stephen Fienberg. Before the Colloquium our esteemed and respected friend and colleague, Stephen Fienberg, unfortunately died and could not witness the outcome of his vision. A PNAS retrospective by Larry Wasserman describes Stephen's extraordinary career (2). The 12 articles in this special issue fall into three categories that shaped the Colloquium. The first \ldots{}  [{$\carriagereturn$}][1]1To whom correspondence should be addressed. Email: shiffrin\{at\}indiana.edu.  [1]: \#xref-corresp-1-1},
  copyright = {\textcopyright{} 2018 . Published under the PNAS license.},
  langid = {english},
  pmid = {29531033},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\K4WNNBBD\\Allison et al_2018_Proceedings of the National Academy of Sciences_Reproducibility of research.pdf;C\:\\Users\\dirko\\Zotero\\storage\\679CVRBY\\2561.html}
}

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}

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  author = {Alpert, G. F. and Hein, G. and Tsai, N. and Naumer, M. J. and Knight, R. T.},
  year = {2008},
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  volume = {28},
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}

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}

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}

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  title = {Differential-{{Geometrical Methods}} in {{Statistics}}},
  author = {Amari, Shun-ichi},
  editor = {Berger, J. and Fienberg, S. and Gani, J. and Krickeberg, K. and Olkin, I. and Singer, B.},
  year = {1985},
  series = {Lecture {{Notes}} in {{Statistics}}},
  volume = {28},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
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}

@book{amari_2016,
  title = {Information {{Geometry}} and {{Its Applications}}},
  author = {Amari, Shun-ichi},
  year = {2016},
  series = {Applied {{Mathematical Sciences}}},
  volume = {194},
  publisher = {{Springer Japan}},
  address = {{Tokyo}},
  doi = {10.1007/978-4-431-55978-8},
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}

@article{amblard_2011,
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  author = {Amblard, Pierre-Olivier and Michel, Olivier J. J.},
  year = {2011},
  month = feb,
  journal = {Journal of Computational Neuroscience},
  volume = {30},
  number = {1},
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  issn = {0929-5313, 1573-6873},
  doi = {10.1007/s10827-010-0231-x},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\UGCRN2D7\\Amblard_Michel_2011_Journal of Computational Neuroscience_On directed information theory and Granger causality graphs.pdf}
}

@article{americanpsychologicalassociation_2009,
  title = {Publication {{Manual}} of the {{American Psychological Association}}},
  author = {American Psychological Association},
  year = {2009},
  pages = {291},
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  annotation = {00013},
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}

@article{amrhein_,
  ids = {amrhein_retire_nodate-1},
  title = {Retire Statistical Significance},
  author = {Amrhein, Valentin and Greenland, Sander and McShane, Blake},
  pages = {3},
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  annotation = {00000},
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}

@article{amrhein_2018,
  title = {Remove, Rather than Redefine, Statistical Significance},
  author = {Amrhein, Valentin and Greenland, Sander},
  year = {2018},
  month = jan,
  journal = {Nature Human Behaviour},
  volume = {2},
  number = {1},
  pages = {4--4},
  issn = {2397-3374},
  doi = {10.1038/s41562-017-0224-0},
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}

@article{anami_2003,
  title = {Stepping Stone Sampling for Retrieving Artifact-Free Electroencephalogram during Functional Magnetic Resonance Imaging},
  author = {Anami, Kimitaka and Mori, Takeyuki and Tanaka, Fumiko and Kawagoe, Yusuke and Okamoto, Jun and Yarita, Masaru and Ohnishi, Takashi and Yumoto, Masato and Matsuda, Hiroshi and Saitoh, Osamu},
  year = {2003},
  month = jun,
  journal = {NeuroImage},
  volume = {19},
  number = {2},
  pages = {281--295},
  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00048-X},
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}

@book{anderson_2003,
  title = {An Introduction to Multivariate Statistical Analysis},
  author = {Anderson, T. W.},
  year = {2003},
  series = {Wiley Series in Probability and Statistics},
  edition = {3rd ed},
  publisher = {{Wiley-Interscience}},
  address = {{Hoboken, N.J}},
  isbn = {978-0-471-36091-9},
  langid = {english},
  lccn = {QA278 .A516 2003},
  keywords = {Multivariate analysis},
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}

@article{andrieu_2003,
  title = {An {{Introduction}} to {{MCMC}} for {{Machine Learning}}},
  author = {Andrieu, Christophe},
  year = {2003},
  pages = {39},
  abstract = {This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.},
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  annotation = {02373},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4MQTBD6P\\Andrieu - 2003 - An Introduction to MCMC for Machine Learning.pdf}
}

@article{annis_2018,
  title = {Bayesian Statistical Approaches to Evaluating Cognitive Models: {{Bayesian}} Statistical Approaches},
  shorttitle = {Bayesian Statistical Approaches to Evaluating Cognitive Models},
  author = {Annis, Jeffrey and Palmeri, Thomas J.},
  year = {2018},
  month = mar,
  journal = {Wiley Interdisciplinary Reviews: Cognitive Science},
  volume = {9},
  number = {2},
  pages = {e1458},
  issn = {19395078},
  doi = {10.1002/wcs.1458},
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}

@article{aquino_2014,
  title = {Deconvolution of Neural Dynamics from {{fMRI}} Data Using a Spatiotemporal Hemodynamic Response Function},
  author = {Aquino, K.M. and Robinson, P.A. and Schira, M.M. and Breakspear, M.},
  year = {2014},
  month = jul,
  journal = {NeuroImage},
  volume = {94},
  pages = {203--215},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2014.03.001},
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}

@article{arbenz_2005,
  title = {Einfuhrung in Matlab1},
  author = {Arbenz, Peter},
  year = {2005},
  annotation = {00000},
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}

@article{archambeau_2011,
  title = {Approximate Inference for Continuous-Time {{Markov}} Processes},
  author = {Archambeau, Cedric and Opper, Manfred},
  year = {2011},
  journal = {Bayesian Time Series Models},
  pages = {125--140},
  annotation = {00000},
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}

@article{ares_,
  title = {{{EUROPEAN COMMISSION DIRECTORATE-GENERAL FOR RESEARCH}} \& {{INNOVATION}}},
  author = {Ares, Ref},
  pages = {9},
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}

@article{artoni_2018,
  title = {Applying Dimension Reduction to {{EEG}} Data by {{Principal Component Analysis}} Reduces the Quality of Its Subsequent {{Independent Component}} Decomposition},
  author = {Artoni, Fiorenzo and Delorme, Arnaud and Makeig, Scott},
  year = {2018},
  month = jul,
  journal = {NeuroImage},
  volume = {175},
  pages = {176--187},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2018.03.016},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XEL5YLDK\\Artoni et al. - 2018 - Applying dimension reduction to EEG data by Princi.pdf}
}

@article{ashburner_2005,
  title = {Unified Segmentation},
  author = {Ashburner, John and Friston, Karl J.},
  year = {2005},
  month = jul,
  journal = {NeuroImage},
  volume = {26},
  number = {3},
  pages = {839--851},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2005.02.018},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VVT5HZ78\\Ashburner_Friston_2005_NeuroImage_Unified segmentation.pdf}
}

@article{ashburner_2007,
  title = {A Fast Diffeomorphic Image Registration Algorithm},
  author = {Ashburner, John},
  year = {2007},
  month = oct,
  journal = {NeuroImage},
  volume = {38},
  number = {1},
  pages = {95--113},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.07.007},
  langid = {english},
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}

@article{ashburner_2010,
  title = {{{VBM}} Tutorial},
  author = {Ashburner, John},
  year = {2010},
  journal = {Tech. repWellcome Trust Centre for Neuroimaging, London, UK},
  annotation = {00000},
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}

@article{ashburner_2012,
  title = {{{SPM}}: {{A}} History},
  shorttitle = {{{SPM}}},
  author = {Ashburner, John},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {2},
  pages = {791--800},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2011.10.025},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\D9D7NJFE\\Ashburner_2012_NeuroImage_SPM.pdf}
}

@article{assecondi_2009,
  title = {Removal of the Ballistocardiographic Artifact from {{EEG}}\textendash{{fMRI}} Data: A Canonical Correlation Approach},
  shorttitle = {Removal of the Ballistocardiographic Artifact from {{EEG}}\textendash{{fMRI}} Data},
  author = {Assecondi, Sara and Hallez, Hans and Staelens, Steven and Bianchi, Anna M and Huiskamp, Geertjan M and Lemahieu, Ignace},
  year = {2009},
  month = mar,
  journal = {Physics in Medicine and Biology},
  volume = {54},
  number = {6},
  pages = {1673--1689},
  issn = {0031-9155, 1361-6560},
  doi = {10.1088/0031-9155/54/6/018},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VBH7V55W\\Assecondi et al_2009_Physics in Medicine and Biology_Removal of the ballistocardiographic artifact from EEG–fMRI data.pdf}
}

@article{assecondi_2015,
  title = {Reliability of {{Information-Based Integration}} of {{EEG}} and {{fMRI Data}}: {{A Simulation Study}}},
  shorttitle = {Reliability of {{Information-Based Integration}} of {{EEG}} and {{fMRI Data}}},
  author = {Assecondi, Sara and Ostwald, Dirk and Bagshaw, Andrew P.},
  year = {2015},
  month = feb,
  journal = {Neural Computation},
  volume = {27},
  number = {2},
  pages = {281--305},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/NECO_a_00695},
  langid = {english},
  annotation = {00003},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YXL46RWM\\Assecondi et al. - 2015 - Reliability of Information-Based Integration of EE.pdf}
}

@article{astrom_1965,
  title = {Optimal Control of {{Markov}} Processes with Incomplete State Information},
  author = {{\AA}str{\"o}m, K.J},
  year = {1965},
  month = feb,
  journal = {Journal of Mathematical Analysis and Applications},
  volume = {10},
  number = {1},
  pages = {174--205},
  issn = {0022247X},
  doi = {10.1016/0022-247X(65)90154-X},
  langid = {english},
  annotation = {00182},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\N9RGCSMU\\Åström - 1965 - Optimal control of Markov processes with incomplet.pdf}
}

@book{attaway_2012,
  title = {{{MATLAB}}: A Practical Introduction to Programming and Problem Solving},
  shorttitle = {{{MATLAB}}},
  author = {Attaway, Stormy},
  year = {2012},
  edition = {2nd ed},
  publisher = {{Butterworth-Heinemann}},
  address = {{Waltham, MA}},
  isbn = {978-0-12-385081-2},
  lccn = {QA297 .A87 2012},
  keywords = {Computer programming,Data processing,MATLAB,Numerical analysis},
  annotation = {OCLC: ocn711864838},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ENUAHB6B\\Attaway_2012_MATLAB.pdf}
}

@article{auer_2002,
  ids = {auer2002},
  title = {Finite-Time Analysis of the Multiarmed Bandit Problem},
  author = {Auer, Peter and {Cesa-Bianchi}, Nicolo and Fischer, Paul},
  year = {2002},
  journal = {Machine learning},
  volume = {47},
  number = {2-3},
  pages = {235--256},
  publisher = {{Springer}},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\X9D8IZAW\\Auer et al_2002_Machine learning_Finite-time analysis of the multiarmed bandit problem.pdf}
}

@article{auksztulewicz_2012,
  title = {Recurrent {{Neural Processing}} and {{Somatosensory Awareness}}},
  author = {Auksztulewicz, R. and Spitzer, B. and Blankenburg, F.},
  year = {2012},
  month = jan,
  journal = {Journal of Neuroscience},
  volume = {32},
  number = {3},
  pages = {799--805},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3974-11.2012},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9S3ZISLR\\Auksztulewicz et al_2012_Journal of Neuroscience_Recurrent Neural Processing and Somatosensory Awareness.pdf}
}

@article{aupperle_2011,
  title = {A Reverse Translational Approach to Quantify Approach-Avoidance Conflict in Humans},
  author = {Aupperle, Robin L. and Sullivan, Sarah and Melrose, Andrew J. and Paulus, Martin P. and Stein, Murray B.},
  year = {2011},
  month = dec,
  journal = {Behavioural Brain Research},
  volume = {225},
  number = {2},
  pages = {455--463},
  issn = {01664328},
  doi = {10.1016/j.bbr.2011.08.003},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ELCAH9N3\\Aupperle et al. - 2011 - A reverse translational approach to quantify appro.pdf}
}

@article{aupperle_2015,
  title = {Neural Substrates of Approach-Avoidance Conflict Decision-Making: {{Approach-Avoidance Conflict}}},
  shorttitle = {Neural Substrates of Approach-Avoidance Conflict Decision-Making},
  author = {Aupperle, Robin L. and Melrose, Andrew J. and Francisco, Alex and Paulus, Martin P. and Stein, Murray B.},
  year = {2015},
  month = feb,
  journal = {Human Brain Mapping},
  volume = {36},
  number = {2},
  pages = {449--462},
  issn = {10659471},
  doi = {10.1002/hbm.22639},
  abstract = {Animal approach-avoidance conflict paradigms have been used extensively to operationalize anxiety, quantify the effects of anxiolytic agents, and probe the neural basis of fear and anxiety. Results from human neuroimaging studies support that a frontal\textendash striatal\textendash amygdala neural circuitry is important for approach-avoidance learning. However, the neural basis of decision-making is much less clear in this context. Thus, we combined a recently developed human approach-avoidance paradigm with functional magnetic resonance imaging (fMRI) to identify neural substrates underlying approach-avoidance conflict decision-making. Fifteen healthy adults completed the approach-avoidance conflict (AAC) paradigm during fMRI. Analyses of variance were used to compare conflict to nonconflict (avoid-threat and approachreward) conditions and to compare level of reward points offered during the decision phase. Trial-by-trial amplitude modulation analyses were used to delineate brain areas underlying decision-making in the context of approach/avoidance behavior. Conflict trials as compared to the nonconflict trials elicited greater activation within bilateral anterior cingulate cortex, anterior insula, and caudate, as well as right dorsolateral prefrontal cortex (PFC). Right caudate and lateral PFC activation was modulated by level of reward offered. Individuals who showed greater caudate activation exhibited less approach behavior. On a trialby-trial basis, greater right lateral PFC activation related to less approach behavior. Taken together, results suggest that the degree of activation within prefrontal-striatal-insula circuitry determines the degree of approach versus avoidance decision-making. Moreover, the degree of caudate and lateral PFC activation related to individual differences in approach-avoidance decision-making. Therefore, the approachavoidance conflict paradigm is ideally suited to probe anxiety-related processing differences during approach-avoidance decision-making. Hum Brain Mapp 36:449\textendash 462, 2015. VC 2014 Wiley Periodicals, Inc.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WG5WQR38\\Aupperle et al. - 2015 - Neural substrates of approach-avoidance conflict d.pdf}
}

@article{averbeck_2017,
  title = {Motivational Neural Circuits Underlying Reinforcement Learning},
  author = {Averbeck, Bruno B. and Costa, Vincent D.},
  year = {2017},
  month = apr,
  journal = {Nature Neuroscience},
  volume = {20},
  number = {4},
  pages = {505--512},
  issn = {1546-1726},
  doi = {10.1038/nn.4506},
  abstract = {Reinforcement learning (RL) is the behavioral process of learning the values of actions and objects. Most models of RL assume that the dopaminergic prediction error signal drives plasticity in frontal\textendash striatal circuits. The striatum then encodes value representations that drive decision processes. However, the amygdala has also been shown to play an important role in forming Pavlovian stimulus\textendash outcome associations. These Pavlovian associations can drive motivated behavior via the amygdala projections to the ventral striatum or the ventral tegmental area. The amygdala may, therefore, play a central role in RL. Here we compare the contributions of the amygdala and the striatum to RL and show that both the amygdala and striatum learn and represent expected values in RL tasks. Furthermore, value representations in the striatum may be inherited, to some extent, from the amygdala. The striatum may, therefore, play less of a primary role in learning stimulus\textendash outcome associations in RL than previously suggested.},
  copyright = {2017 Nature Publishing Group},
  langid = {english},
  annotation = {00019},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GQQY8X7D\\Averbeck_Costa_2017_Nature Neuroscience_Motivational neural circuits underlying reinforcement learning.pdf;C\:\\Users\\dirko\\Zotero\\storage\\ZY3MGEXS\\nn.html}
}

@article{babajani_2005,
  title = {Integrated {{MEG}} and {{fMRI Model}}: {{Synthesis}} and {{Analysis}}},
  shorttitle = {Integrated {{MEG}} and {{fMRI Model}}},
  author = {Babajani, Abbas and Nekooei, Mohammad-Hossein and {Soltanian-Zadeh}, Hamid},
  year = {2005},
  month = dec,
  journal = {Brain Topography},
  volume = {18},
  number = {2},
  pages = {101--113},
  issn = {0896-0267, 1573-6792},
  doi = {10.1007/s10548-005-0279-5},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3SCIV4P9\\Babajani et al_2005_Brain Topography_Integrated MEG and fMRI Model.pdf}
}

@article{babajani_2006,
  title = {Integrated {{MEG}}/{{EEG}} and {{fMRI}} Model Based on Neural Masses},
  author = {Babajani, A. and {Soltanian-Zadeh}, H.},
  year = {2006},
  month = sep,
  journal = {IEEE Transactions on Biomedical Engineering},
  volume = {53},
  number = {9},
  pages = {1794--1801},
  issn = {0018-9294, 1558-2531},
  doi = {10.1109/TBME.2006.873748},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3RAX4ZLG\\Babajani_Soltanian-Zadeh_2006_IEEE Transactions on Biomedical Engineering_Integrated MEG-EEG and fMRI model based on neural masses.pdf}
}

@article{babayan_2018,
  title = {Belief State Representation in the Dopamine System},
  author = {Babayan, Benedicte M. and Uchida, Naoshige and Gershman, Samuel. J.},
  year = {2018},
  journal = {Nature Communications},
  volume = {9},
  number = {1},
  pages = {1891},
  issn = {2041-1723},
  doi = {10.1038/s41467-018-04397-0},
  abstract = {Learning to predict future outcomes is critical for driving appropriate behaviors. Reinforcement learning (RL) models have successfully accounted for such learning, relying on reward prediction errors (RPEs) signaled by midbrain dopamine neurons. It has been proposed that when sensory data provide only ambiguous information about which state an animal is in, it can predict reward based on a set of probabilities assigned to hypothetical states (called the belief state). Here we examine how dopamine RPEs and subsequent learning are regulated under state uncertainty. Mice are first trained in a task with two potential states defined by different reward amounts. During testing, intermediate-sized rewards are given in rare trials. Dopamine activity is a non-monotonic function of reward size, consistent with RL models operating on belief states. Furthermore, the magnitude of dopamine responses quantitatively predicts changes in behavior. These results establish the critical role of state inference in RL.},
  keywords = {Learning algorithms,Reward}
}

@article{bach_,
  title = {A {{Probabilistic Interpretation}} of {{Canonical Correlation Analysis}}},
  author = {Bach, Francis R and Jordan, Michael I},
  pages = {11},
  abstract = {We give a probabilistic interpretation of canonical correlation (CCA) analysis as a latent variable model for two Gaussian random vectors. Our interpretation is similar to the probabilistic interpretation of principal component analysis (Tipping and Bishop, 1999, Roweis, 1998). In addition, we can interpret Fisher linear discriminant analysis (LDA) as CCA between appropriately defined vectors.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HJ78CAI3\\Bach und Jordan - A Probabilistic Interpretation of Canonical Correl.pdf}
}

@article{bach_2012,
  ids = {bach2012,bach_knowing_2012-1},
  title = {Knowing How Much You Don't Know: A Neural Organization of Uncertainty Estimates},
  shorttitle = {Knowing How Much You Don't Know},
  author = {Bach, Dominik R. and Dolan, Raymond J.},
  year = {2012},
  month = aug,
  journal = {Nature Reviews Neuroscience},
  volume = {13},
  number = {8},
  pages = {572--586},
  publisher = {{Nature Publishing Group}},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/nrn3289},
  langid = {english},
  pmid = {22781958},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZC8QNKVS\\Bach_Dolan_2012_Nature Reviews Neuroscience_Knowing how much you don't know.pdf}
}

@article{bachmaier_2008,
  title = {Variogram or Semivariogram? {{Understanding}} the Variances in a Variogram},
  shorttitle = {Variogram or Semivariogram?},
  author = {Bachmaier, Martin and Backes, Matthias},
  year = {2008},
  month = jun,
  journal = {Precision Agriculture},
  volume = {9},
  number = {3},
  pages = {173--175},
  issn = {1385-2256, 1573-1618},
  doi = {10.1007/s11119-008-9056-2},
  langid = {english},
  annotation = {00059},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MGMRHB8P\\Bachmaier and Backes - 2008 - Variogram or semivariogram Understanding the vari.pdf}
}

@article{bagshaw_2007,
  title = {Single Trial Variability of {{EEG}} and {{fMRI}} Responses to Visual Stimuli},
  author = {Bagshaw, Andrew P. and Warbrick, Tracy},
  year = {2007},
  month = nov,
  journal = {NeuroImage},
  volume = {38},
  number = {2},
  pages = {280--292},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.07.042},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\63NJH5QX\\Bagshaw_Warbrick_2007_NeuroImage_Single trial variability of EEG and fMRI responses to visual stimuli.pdf}
}

@article{baillet_2001,
  title = {Electromagnetic Brain Mapping},
  author = {Baillet, Sylvain and Mosher, John C. and Leahy, Richard M.},
  year = {2001},
  journal = {IEEE Signal processing magazine},
  volume = {18},
  number = {6},
  pages = {14--30},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\D2YUPDDS\\Baillet et al_2001_IEEE Signal processing magazine_Electromagnetic brain mapping.pdf}
}

@article{baird_2002,
  title = {Frontal {{Lobe Activation}} during {{Object Permanence}}: {{Data}} from {{Near-Infrared Spectroscopy}}},
  shorttitle = {Frontal {{Lobe Activation}} during {{Object Permanence}}},
  author = {Baird, A},
  year = {2002},
  month = aug,
  journal = {NeuroImage},
  volume = {16},
  number = {4},
  pages = {1120--1126},
  issn = {10538119},
  doi = {10.1006/nimg.2002.1170},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\73AY7IQ9\\Baird_2002_NeuroImage_Frontal Lobe Activation during Object Permanence.pdf}
}

@article{balaguer_2016,
  title = {Neural {{Mechanisms}} of {{Hierarchical Planning}} in a {{Virtual Subway Network}}},
  author = {Balaguer, Jan and Spiers, Hugo and Hassabis, Demis and Summerfield, Christopher},
  year = {2016},
  month = may,
  journal = {Neuron},
  volume = {90},
  number = {4},
  pages = {893--903},
  issn = {08966273},
  doi = {10.1016/j.neuron.2016.03.037},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\IATWJ4E4\\Balaguer_Neuron_16.pdf}
}

@article{balasubramani_2015,
  title = {A Network Model of Basal Ganglia for Understanding the Roles of Dopamine and Serotonin in Reward-Punishment-Risk Based Decision Making},
  author = {Balasubramani, Pragathi P. and Chakravarthy, V. Srinivasa and Ravindran, Balaraman and Moustafa, Ahmed A.},
  year = {2015},
  journal = {Frontiers in Computational Neuroscience},
  volume = {9},
  issn = {1662-5188},
  doi = {10.3389/fncom.2015.00076},
  abstract = {There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making as well. Despite this evidence, little is known about the computational principles and neural correlates of risk computation in this subcortical system. We have previously proposed a reinforcement learning based model of the BG that simulates the interactions between dopamine (DA) and serotonin (5HT) in a diverse set of experimental effects including reward, punishment and risk based decision making. Starting with the idea that the activity of DA represents reward prediction error, the model posits that serotoninergic activity in the striatum controls risk-prediction error. Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data. In this work, we expand the earlier model into a detailed network model of the BG and demonstrate the joint contributions of DA-5HT in risk and reward-punishment sensitivity. At the core of the proposed network model is the following insight regarding cellular correlates of value and risk computation. Just as DA D1 receptor (D1R) expressing medium spiny neurons (MSNs) of the striatum were thought to be neural substrates for value computation, we propose that DA D1R and D2R co-expressing MSNs, reported to occupy a significant proportion of the striatum and are implicated in disorders like schizophrenia and drug addiction, are capable of computing risk. Ours is the first-of-its-kind model that accounts for the significant computational possibilities of these co-expressing D1R-D2R MSNs, and describes how DA-5HT mediated activity in these classes of neurons (D1R-, D2R-, D1R-D2R- MSNs) contribute to the BG dynamics. We also apply the model to capture the behaviour of PD patients in a probabilistic learning paradigm. The study observes that optimizing 5HT levels along with DA medication could be essential to improving the patients' learning},
  langid = {english},
  keywords = {Basal Ganglia network,D1 and D2 receptor co-expression,Decision Making,Dopamine,Medium spiny neurons,Punishment,reinforcement learning,Reward,risk,Serotonin},
  annotation = {00018},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BRDKCB45\\Balasubramani et al_2015_Frontiers in Computational Neuroscience_A network model of basal ganglia for understanding the roles of dopamine and.pdf}
}

@article{baldeweg_2004,
  title = {Mismatch Negativity Potentials and Cognitive Impairment in Schizophrenia},
  author = {Baldeweg, Torsten and Klugman, Anthony and Gruzelier, John and Hirsch, Steven R},
  year = {2004},
  month = aug,
  journal = {Schizophrenia Research},
  volume = {69},
  number = {2-3},
  pages = {203--217},
  issn = {09209964},
  doi = {10.1016/j.schres.2003.09.009},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MC8B6UPG\\Baldeweg et al_2004_Schizophrenia Research_Mismatch negativity potentials and cognitive impairment in schizophrenia.pdf}
}

@inproceedings{baldi_2005,
  title = {Attention: Bits versus Wows},
  shorttitle = {Attention},
  booktitle = {Neural {{Networks}} and {{Brain}}, 2005. {{ICNN}}\&{{B}}'05. {{International Conference}} On},
  author = {Baldi, Pierre and Itti, Laurent},
  year = {2005},
  volume = {1},
  pages = {PL--PL},
  publisher = {{IEEE}},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MTHFEAMS\\Baldi_Itti_2005_Neural Networks and Brain, 2005. ICNN&B'05. International Conference on_Attention.pdf}
}

@article{ballerini_2008,
  title = {Interaction Ruling Animal Collective Behavior Depends on Topological Rather than Metric Distance: {{Evidence}} from a Field Study},
  shorttitle = {Interaction Ruling Animal Collective Behavior Depends on Topological Rather than Metric Distance},
  author = {Ballerini, Michele and Cabibbo, Nicola and Candelier, Raphael and Cavagna, Andrea and Cisbani, Evaristo and Giardina, Irene and Lecomte, Vivien and Orlandi, Alberto and Parisi, Giorgio and Procaccini, Andrea},
  year = {2008},
  journal = {Proceedings of the national academy of sciences},
  volume = {105},
  number = {4},
  pages = {1232--1237},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UCJM5L9V\\Ballerini et al_2008_Proceedings of the national academy of sciences_Interaction ruling animal collective behavior depends on topological rather.pdf}
}

@article{bandettini_2012,
  title = {Twenty Years of Functional {{MRI}}: {{The}} Science and the Stories},
  shorttitle = {Twenty Years of Functional {{MRI}}},
  author = {Bandettini, Peter A.},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {2},
  pages = {575--588},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.04.026},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SXNZ7X7E\\Bandettini_2012_NeuroImage_Twenty years of functional MRI.pdf}
}

@inproceedings{banerjee_2006,
  title = {Convex Optimization Techniques for Fitting Sparse {{Gaussian}} Graphical Models},
  booktitle = {Proceedings of the 23rd International Conference on {{Machine}} Learning},
  author = {Banerjee, Onureena and Ghaoui, Laurent El and {d'Aspremont}, Alexandre and Natsoulis, Georges},
  year = {2006},
  pages = {89--96},
  publisher = {{ACM}},
  annotation = {00000},
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}

@article{bansal_2018,
  title = {Cluster-Level Statistical Inference in {{fMRI}} Datasets: {{The}} Unexpected Behavior of Random Fields in High Dimensions},
  shorttitle = {Cluster-Level Statistical Inference in {{fMRI}} Datasets},
  author = {Bansal, Ravi and Peterson, Bradley S.},
  year = {2018},
  month = jun,
  journal = {Magnetic Resonance Imaging},
  volume = {49},
  pages = {101--115},
  issn = {0730725X},
  doi = {10.1016/j.mri.2018.01.004},
  abstract = {Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EWZEPIX8\\Bansal und Peterson - 2018 - Cluster-level statistical inference in fMRI datase.pdf}
}

@article{barbati_2006,
  title = {Functional Source Separation from Magnetoencephalographic Signals},
  author = {Barbati, Giulia and Sigismondi, Roberto and Zappasodi, Filippo and Porcaro, Camillo and Graziadio, Sara and Valente, Giancarlo and Balsi, Marco and Rossini, Paolo Maria and Tecchio, Franca},
  year = {2006},
  month = dec,
  journal = {Human Brain Mapping},
  volume = {27},
  number = {12},
  pages = {925--934},
  issn = {10659471},
  doi = {10.1002/hbm.20232},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9URBAKBG\\Barbati et al_2006_Human Brain Mapping_Functional source separation from magnetoencephalographic signals.pdf}
}

@article{barber_,
  title = {Unified {{Inference}} for {{Variational Bayesian Linear Gaussian State-Space Models}}},
  author = {Barber, David and Chiappa, Silvia},
  pages = {8},
  abstract = {Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefore of considerable interest. The approximate Variational Bayesian method applied to these models is an attractive approach, used successfully in applications ranging from acoustics to bioinformatics. The most challenging aspect of implementing the method is in performing inference on the hidden state sequence of the model. We show how to convert the inference problem so that standard Kalman Filtering/Smoothing recursions from the literature may be applied. This is in contrast to previously published approaches based on Belief Propagation. Our framework both simplifies and unifies the inference problem, so that future applications may be more easily developed. We demonstrate the elegance of the approach on Bayesian temporal ICA, with an application to finding independent dynamical processes underlying noisy EEG signals.},
  annotation = {00041},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MDIJITS4\\Barber and Chiappa - Uniﬁed Inference for Variational Bayesian Linear G.pdf}
}

@book{barber_2011,
  title = {Bayesian Time Series Models},
  editor = {Barber, David and Cemgil, Ali Taylan and Chiappa, Silvia},
  year = {2011},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge, UK ; New York}},
  isbn = {978-0-521-19676-5},
  langid = {english},
  lccn = {QA280 .B39 2011},
  keywords = {Bayesian statistical decision theory,Time-series analysis},
  annotation = {00242  OCLC: ocn710815923},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4MZTLBJQ\\Barber et al. - 2011 - Bayesian time series models.pdf}
}

@book{barber_2012,
  title = {Bayesian Reasoning and Machine Learning},
  author = {Barber, David},
  year = {2012},
  publisher = {{Cambridge University Press}},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6RY7MRP7\\Barber_2012_Bayesian reasoning and machine learning.pdf}
}

@article{barnes_2013,
  title = {Set-Level Threshold-Free Tests on the Intrinsic Volumes of {{SPMs}}},
  author = {Barnes, Gareth R. and Ridgway, Gerard R. and Flandin, Guillaume and Woolrich, Mark and Friston, Karl},
  year = {2013},
  month = mar,
  journal = {NeuroImage},
  volume = {68},
  pages = {133--140},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.11.046},
  abstract = {Conventionally, set-level inference on statistical parametric maps (SPMs) is based on the topological features of an excursion set above some threshold\textemdash for example, the number of clusters or Euler characteristic. The expected Euler characteristic\textemdash under the null hypothesis\textemdash can be predicted from an intrinsic measure or volume of the SPM, such as the resel counts or the Lipschitz\textendash Killing curvatures (LKC). We propose a new approach that performs a null hypothesis omnibus test on an SPM, by testing whether its intrinsic volume (described by LKC coefficients) is different from the volume of the underlying residual fields: intuitively, whether the number of peaks in the statistical field (testing for signal) and the residual fields (noise) are consistent or not. Crucially, this new test requires no arbitrary feature-defining threshold but is nevertheless sensitive to distributed or spatially extended patterns. We show the similarities between our approach and conventional topological inference\textemdash in terms of false positive rate control and sensitivity to treatment effects\textemdash in two and three dimensional simulations. The test consistently improves on classical approaches for moderate ({$>$} 20) degrees of freedom. We also demonstrate the application to real data and illustrate the comparison of the expected and observed Euler characteristics over the complete threshold range.},
  langid = {english},
  annotation = {00003},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\R2P55RKM\\Barnes et al_2013_NeuroImage_Set-level threshold-free tests on the intrinsic volumes of SPMs.pdf}
}

@article{barnes_2017,
  title = {Reconciling {{Personal Data Consent Practices}} in {{Clinical Trials}} with the {{EU General Data Protection Regulation}}},
  author = {BARNES, MARK and MASSEY, ROHAN},
  year = {2017},
  pages = {5},
  annotation = {00000},
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}

@article{barnett_2009,
  title = {Granger {{Causality}} and {{Transfer Entropy Are Equivalent}} for {{Gaussian Variables}}},
  author = {Barnett, Lionel and Barrett, Adam B. and Seth, Anil K.},
  year = {2009},
  month = dec,
  journal = {Physical Review Letters},
  volume = {103},
  number = {23},
  issn = {0031-9007, 1079-7114},
  doi = {10.1103/PhysRevLett.103.238701},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GZQG4LWR\\Barnett et al_2009_Physical Review Letters_Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables.pdf}
}

@article{barrett_2011,
  title = {Practical Measures of Integrated Information for Time-Series Data},
  author = {Barrett, Adam B. and Seth, Anil K.},
  year = {2011},
  journal = {PLoS computational biology},
  volume = {7},
  number = {1},
  pages = {e1001052},
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}

@inproceedings{bartlett_1937,
  title = {Properties of Sufficiency and Statistical Tests},
  booktitle = {Proc. {{R}}. {{Soc}}. {{Lond}}. {{A}}},
  author = {Bartlett, Maurice S.},
  year = {1937},
  volume = {160},
  pages = {268--282},
  publisher = {{The Royal Society}},
  annotation = {00000},
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}

@book{bartling_2014,
  title = {Opening {{Science}}},
  editor = {Bartling, S{\"o}nke and Friesike, Sascha},
  year = {2014},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-319-00026-8},
  isbn = {978-3-319-00025-1 978-3-319-00026-8},
  langid = {english},
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}

@article{barto_1995,
  title = {Learning to Act Using Real-Time Dynamic Programming},
  author = {Barto, Andrew G. and Bradtke, Steven J. and Singh, Satinder P.},
  year = {1995},
  journal = {Artificial intelligence},
  volume = {72},
  number = {1-2},
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}

@article{bartolo_2020,
  ids = {bartolo2020},
  title = {Prefrontal {{Cortex Predicts State Switches}} during {{Reversal Learning}}},
  author = {Bartolo, Ramon and Averbeck, Bruno B.},
  year = {2020},
  month = apr,
  journal = {Neuron},
  pages = {S0896627320302312},
  issn = {08966273},
  doi = {10.1016/j.neuron.2020.03.024},
  abstract = {Reinforcement learning allows organisms to predict future outcomes and to update their beliefs about value in the world. The dorsal-lateral prefrontal cortex (dlPFC) integrates information carried by reward circuits, which can be used to infer the current state of the world under uncertainty. Here, we explored the dlPFC computations related to updating current beliefs during stochastic reversal learning. We recorded the activity of populations up to 1,000 neurons, simultaneously, in two male macaques while they executed a two-armed bandit reversal learning task. Behavioral analyses using a Bayesian framework showed that animals inferred reversals and switched their choice preference rapidly, rather than slowly updating choice values, consistent with state inference. Furthermore, dlPFC neural populations accurately encoded choice preference switches. These results suggest that prefrontal neurons dynamically encode decisions associated with Bayesian subjective values, highlighting the role of the PFC in representing a belief about the current state of the world.},
  langid = {english},
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}

@book{barwolff_2017,
  title = {{H\"ohere Mathematik f\"ur Naturwissenschaftler und Ingenieure}},
  author = {B{\"a}rwolff, G{\"u}nter},
  year = {2017},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-662-55022-9},
  isbn = {978-3-662-55021-2 978-3-662-55022-9},
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}

@book{basilevsky_1994,
  title = {Statistical Factor Analysis and Related Methods: Theory and Applications},
  shorttitle = {Statistical Factor Analysis and Related Methods},
  author = {Basilevsky, Alexander},
  year = {1994},
  series = {Wiley Series in Probability and Mathematical Statistics},
  publisher = {{Wiley}},
  address = {{New York}},
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}

@article{bassett_2020,
  title = {Reflections on the Past Two Decades of Neuroscience},
  author = {Bassett, Danielle S. and Cullen, Kathleen E. and Eickhoff, Simon B. and Farah, Martha J. and Goda, Yukiko and Haggard, Patrick and Hu, Hailan and Hurd, Yasmin L. and Josselyn, Sheena A. and Khakh, Baljit S. and Knoblich, J{\"u}rgen A. and Poirazi, Panayiota and Poldrack, Russell A. and Prinz, Marco and Roelfsema, Pieter R. and {Spires-Jones}, Tara L. and Sur, Mriganka and Ueda, Hiroki R.},
  year = {2020},
  month = sep,
  journal = {Nature Reviews Neuroscience},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/s41583-020-0363-6},
  abstract = {The first issue of Nature Reviews Neuroscience was published 20 years ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selection of researchers from across the field who have authored pieces published in the journal in recent years for their thoughts on notable and interesting developments in neuroscience, and particularly in their areas of the field, over the past two decades. They also provide some thoughts on current lines of research and questions that excite them.},
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}

@article{basten_2010,
  title = {How the Brain Integrates Costs and Benefits during Decision Making},
  author = {Basten, U. and Biele, G. and Heekeren, H. R. and Fiebach, C. J.},
  year = {2010},
  month = dec,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {107},
  number = {50},
  pages = {21767--21772},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0908104107},
  langid = {english},
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}

@article{bastos_2012,
  title = {Canonical {{Microcircuits}} for {{Predictive Coding}}},
  author = {Bastos, Andre~M. and Usrey, W.~Martin and Adams, Rick~A. and Mangun, George~R. and Fries, Pascal and Friston, Karl~J.},
  year = {2012},
  month = nov,
  journal = {Neuron},
  volume = {76},
  number = {4},
  pages = {695--711},
  issn = {08966273},
  doi = {10.1016/j.neuron.2012.10.038},
  langid = {english},
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}

@book{bauer_2001,
  title = {Measure and Integration Theory},
  author = {Bauer, Heinz},
  year = {2001},
  series = {De {{Gruyter}} Studies in Mathematics},
  number = {26},
  publisher = {{W. de Gruyter}},
  address = {{Berlin ; New York}},
  isbn = {978-3-11-016719-1},
  langid = {english},
  lccn = {QC20.7.M43 B4813 2001},
  keywords = {Integrals; Generalized,Measure theory},
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}

@book{bauerle_2011,
  title = {Markov {{Decision Processes}} with {{Applications}} to {{Finance}}},
  author = {B{\"a}uerle, Nicole and Rieder, Ulrich},
  year = {2011},
  series = {Universitext},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-18324-9},
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}

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}

@article{baum_1968,
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@misc{bdsg_2017,
  title = {Gesetz Zur {{Anpassung}} Des {{Datenschutzrechts}} an Die {{Verordnung}} ({{EU}}) 2016/679 Und Zur {{Umsetzung}} Der {{Richtlinie}} ({{EU}}) 2016/680},
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@article{beal_2003,
  title = {The {{Variational Bayesian EM Algorithm}} for {{Incomplete Data}}: With {{Application}} to {{Scoring Graphical Model Structures}}},
  author = {Beal, Matthew James and Ghahramani, Zoubin},
  year = {2003},
  pages = {10},
  abstract = {We present an efficient procedure for estimating the marginal likelihood of probabilistic models with latent variables or incomplete data. This method constructs and optimises a lower bound on the marginal likelihood using variational calculus, resulting in an iterative algorithm which generalises the EM algorithm by maintaining posterior distributions over both latent variables and parameters. We define the family of conjugate-exponential models\textemdash which includes finite mixtures of exponential family models, factor analysis, hidden Markov models, linear state-space models, and other models of interest\textemdash for which this bound on the marginal likelihood can be computed very simply through a modification of the standard EM algorithm. In particular, we focus on applying these bounds to the problem of scoring discrete directed graphical model structures (Bayesian networks). Extensive simulations comparing the variational bounds to the usual approach based on the Bayesian Information Criterion (BIC) and to a sampling-based gold standard method known as Annealed Importance Sampling (AIS) show that variational bounds substantially outperform BIC in finding the correct model structure at relatively little computational cost, while approaching the performance of the much more costly AIS procedure. Using AIS allows us to provide the first serious case study of the tightness of variational bounds. We also analyse the perfomance of AIS through a variety of criteria, discuss the use of other variational approaches to estimating marginal likelihoods based on Bethe and Kikuchi approximations, and outline directions in which this work can be extended.},
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}

@book{beal_2003a,
  title = {Variational Algorithms for Approximate {{Bayesian}} Inference},
  author = {Beal, Matthew James},
  year = {2003},
  publisher = {{university of London London}},
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}

@article{beattie_2016,
  title = {{{DeepMind Lab}}},
  author = {Beattie, Charles and Leibo, Joel Z. and Teplyashin, Denis and Ward, Tom and Wainwright, Marcus and K{\"u}ttler, Heinrich and Lefrancq, Andrew and Green, Simon and Vald{\'e}s, V{\'i}ctor and Sadik, Amir and Schrittwieser, Julian and Anderson, Keith and York, Sarah and Cant, Max and Cain, Adam and Bolton, Adrian and Gaffney, Stephen and King, Helen and Hassabis, Demis and Legg, Shane and Petersen, Stig},
  year = {2016},
  month = dec,
  journal = {arXiv:1612.03801 [cs]},
  eprint = {1612.03801},
  eprinttype = {arxiv},
  primaryclass = {cs},
  abstract = {DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Artificial Intelligence},
  annotation = {00079},
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}

@book{beazley_2013,
  title = {Python Cookbook},
  author = {Beazley, David M. and Jones, Brian K.},
  year = {2013},
  edition = {Third edition},
  publisher = {{O'Reilly}},
  address = {{Sebastopol, CA}},
  isbn = {978-1-4493-4037-7},
  langid = {english},
  lccn = {QA76.73.P98 B42 2013},
  keywords = {Python (Computer program language)},
  annotation = {00033  OCLC: ocn826659391},
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}

@article{bechara_1997,
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  author = {Bechara, Antoine and Damasio, Hanna and Tranel, Daniel and Damasio, Antonio R.},
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  number = {6},
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@book{becker_1988,
  title = {The New {{S}} Language: A Programming Environment for Data Analysis and Graphics},
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  publisher = {{Chapman \& Hall}},
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@incollection{becker_1994,
  title = {A {{Brief History}} of {{S}}},
  booktitle = {Computational {{Statistics}}},
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@book{becker_1996,
  title = {The New {{S}} Language: A Programming Environment for Data Analysis and Graphics},
  shorttitle = {The New {{S}} Language},
  author = {Becker, Richard A. and Chambers, John M. and Wilks, Allan R. and Wilks, Allen Reeve},
  year = {1996},
  edition = {Reprint},
  publisher = {{Chapman \& Hall}},
  address = {{Boca Raton}},
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}

@article{becker_2011,
  title = {How {{Ongoing Neuronal Oscillations Account}} for {{Evoked fMRI Variability}}},
  author = {Becker, R. and Reinacher, M. and Freyer, F. and Villringer, A. and Ritter, P.},
  year = {2011},
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@book{beekmann_1998,
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@book{beekmann_1999,
  title = {{Analysis II}},
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  year = {1999},
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@article{behrends_2010,
  title = {Mathematische {{Statistik}}},
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@article{behrens_2003,
  title = {Characterization and Propagation of Uncertainty in Diffusion-Weighted {{MR}} Imaging},
  author = {Behrens, T.E.J. and Woolrich, M.W. and Jenkinson, M. and {Johansen-Berg}, H. and Nunes, R.G. and Clare, S. and Matthews, P.M. and Brady, J.M. and Smith, S.M.},
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  number = {5},
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  doi = {10.1002/mrm.10609},
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}

@article{behrens_2007,
  title = {Learning the Value of Information in an Uncertain World},
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  journal = {Nature Neuroscience},
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}

@incollection{beimgraben_2007,
  title = {Foundations of Neurophysics},
  booktitle = {Lectures in {{Supercomputational Neurosciences}}},
  author = {{beim Graben}, Peter},
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}

@article{beimgraben_2008,
  title = {Simulating Global Properties of Electroencephalograms with Minimal Random Neural Networks},
  author = {{beim Graben}, Peter and Kurths, J{\"u}rgen},
  year = {2008},
  month = jan,
  journal = {Neurocomputing},
  volume = {71},
  number = {4-6},
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}

@article{belitski_2008,
  title = {Low-{{Frequency Local Field Potentials}} and {{Spikes}} in {{Primary Visual Cortex Convey Independent Visual Information}}},
  author = {Belitski, A. and Gretton, A. and Magri, C. and Murayama, Y. and Montemurro, M. A. and Logothetis, N. K. and Panzeri, S.},
  year = {2008},
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  journal = {Journal of Neuroscience},
  volume = {28},
  number = {22},
  pages = {5696--5709},
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  doi = {10.1523/JNEUROSCI.0009-08.2008},
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}

@article{belkin_2019,
  title = {Reconciling Modern Machine-Learning Practice and the Classical Bias\textendash Variance Trade-Off},
  author = {Belkin, Mikhail and Hsu, Daniel and Ma, Siyuan and Mandal, Soumik},
  year = {2019},
  month = aug,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {116},
  number = {32},
  pages = {15849--15854},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1903070116},
  abstract = {Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias\textendash variance trade-off, appears to be at odds with the observed behavior of methods used in modern machine-learning practice. The bias\textendash variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple enough to avoid fitting spurious patterns. However, in modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered overfitted, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This ``double-descent'' curve subsumes the textbook U-shaped bias\textendash variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine-learning models delineates the limits of classical analyses and has implications for both the theory and the practice of machine learning.},
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}

@article{bell_1995,
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  author = {Bell, Anthony J and Sejnowski, T.},
  year = {1995},
  journal = {Neural Computation},
  volume = {7},
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}

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  author = {Bellman, Richard},
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}

@book{bellman_2003,
  title = {Dynamic Programming},
  author = {Bellman, Richard},
  year = {2003},
  edition = {Dover ed},
  publisher = {{Dover Publications}},
  address = {{Mineola, N.Y}},
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}

@article{bembich_2014,
  title = {Differences in Time Course Activation of Dorsolateral Prefrontal Cortex Associated with Low or High Risk Choices in a Gambling Task},
  author = {Bembich, Stefano and Clarici, Andrea and Vecchiet, Cristina and Baldassi, Giulio and Cont, Gabriele and Demarini, Sergio},
  year = {2014},
  month = jun,
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  issn = {1662-5161},
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}

@book{ben-akiva_1985,
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  shorttitle = {Discrete Choice Analysis},
  author = {{Ben-Akiva}, Moshe E. and Lerman, Steven R.},
  year = {1985},
  series = {{{MIT Press}} Series in Transportation Studies},
  number = {9},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-02217-0},
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  lccn = {HE336.C5 B46 1985},
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}

@article{ben-akiva_1986,
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  author = {{Ben-Akiva}, Moshe and Swait, Joffre},
  year = {1986},
  month = may,
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}

@article{ben-simon_2008,
  title = {Never {{Resting Brain}}: {{Simultaneous Representation}} of {{Two Alpha Related Processes}} in {{Humans}}},
  shorttitle = {Never {{Resting Brain}}},
  author = {{Ben-Simon}, Eti and Podlipsky, Ilana and Arieli, Amos and Zhdanov, Andrey and Hendler, Talma},
  editor = {Gendelman, Howard E.},
  year = {2008},
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}

@article{benjamin_2018,
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@article{benjamini_2010,
  title = {Discovering the False Discovery Rate: {{False Discovery Rate}}},
  shorttitle = {Discovering the False Discovery Rate},
  author = {Benjamini, Yoav},
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  month = aug,
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume = {72},
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  issn = {13697412},
  doi = {10.1111/j.1467-9868.2010.00746.x},
  abstract = {I describe the background for the paper `Controlling the false discovery rate: a new and powerful approach to multiple comparisons' by Benjamini and Hochberg that was published in the Journal of the Royal Statistical Society , Series B, in 1995. I review the progress since made on the false discovery rate, as well as the major conceptual developments that followed.},
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}

@article{bennett_2015,
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  journal = {eNeuro},
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  doi = {10.1523/ENEURO.0076-15.2015},
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}

@article{berger_1934,
  title = {{\"Uber das Elektrenkephalogramm des Menschen}},
  author = {Berger, Hans},
  year = {1934},
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@book{berger_1988,
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@inproceedings{berger_2003,
  title = {Hierarchical {{Bayesian}} Models for Applications in Information Retrieval},
  booktitle = {Bayesian {{Statistics}} 7: {{Proceedings}} of the {{Seventh Valencia International Meeting}}},
  author = {Berger, JM Bernardo MJ Bayarri JO and Dawid, A. P. and Smith, D. Heckerman AFM and West, M.},
  year = {2003},
  pages = {25},
  publisher = {{Oxford University Press}},
  annotation = {00051},
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}

@article{bernard_2005,
  title = {An Introduction to the Imprecise {{Dirichlet}} Model for Multinomial Data},
  author = {Bernard, Jean-Marc},
  year = {2005},
  month = jun,
  journal = {International Journal of Approximate Reasoning},
  volume = {39},
  number = {2-3},
  pages = {123--150},
  issn = {0888613X},
  doi = {10.1016/j.ijar.2004.10.002},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\63R6LE2S\\Bernard_2005_International Journal of Approximate Reasoning_An introduction to the imprecise Dirichlet model for multinomial data.pdf}
}

@incollection{bernard_2008,
  title = {Imprecise {{Probabilistic Prediction}} for {{Categorical Data}}: {{From Bayesian Inference}} to the {{Imprecise Dirichlet-Multinomial Model}}},
  shorttitle = {Imprecise {{Probabilistic Prediction}} for {{Categorical Data}}},
  booktitle = {Soft {{Methods}} for {{Handling Variability}} and {{Imprecision}}},
  author = {Bernard, Jean-Marc},
  editor = {Dubois, Didier and Lubiano, M. Asunci{\'o}n and Prade, Henri and Gil, Mar{\'i}a {\'A}ngeles and Grzegorzewski, Przemys{\l}aw and Hryniewicz, Olgierd},
  year = {2008},
  volume = {48},
  pages = {3--9},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-540-85027-4_1},
  abstract = {From n categorical observations, what can be predicted about the next n ones? We present a generalization of the Bayesian approach, the imprecise Dirichlet-multinomial model (IDMM), which uses sets of Dirichlet-multinomial distributions to model prior ignorance. The IDMM satisfies coherence, symmetry and several desirable invariance properties.},
  isbn = {978-3-540-85026-7 978-3-540-85027-4},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QILWJRWP\\Bernard - 2008 - Imprecise Probabilistic Prediction for Categorical.pdf}
}

@article{bernard_2008a,
  title = {Predictive Inference: {{From Bayesian}} Inference to {{Imprecise Probability}}},
  author = {Bernard, Jean-Marc},
  year = {2008},
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}

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  title = {Bayesian Theory},
  author = {Bernardo, J. M. and Smith, Adrian F. M.},
  year = {1994},
  series = {Wiley Series in Probability and Mathematical Statistics},
  publisher = {{Wiley}},
  address = {{Chichester, Eng. ; New York}},
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}

@article{bernardo_2003,
  title = {Bayesian {{Statistics}}},
  author = {Bernardo, Jos{\'e} M},
  year = {2003},
  pages = {46},
  abstract = {Mathematical statistics uses two major paradigms, conventional (or frequentist), and Bayesian. Bayesian methods provide a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods may be derived from an axiomatic system, and hence provide a general, coherent methodology. Bayesian methods contain as particular cases many of the more often used frequentist procedures, solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. In particular, Bayesian methods make it possible to incorporate scientific hypothesis in the analysis (by means of the prior distribution) and may be applied to problems whose structure is too complex for conventional methods to be able to handle. The Bayesian paradigm is based on an interpretation of probability as a rational, conditional measure of uncertainty, which closely matches the sense of the word `probability' in ordinary language. Statistical inference about a quantity of interest is described as the modification of the uncertainty about its value in the light of evidence, and Bayes' theorem precisely specifies how this modification should be made. The special situation, often met in scientific reporting and public decision making, where the only acceptable information is that which may be deduced from available documented data, is addressed by objective Bayesian methods, as a particular case.},
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}

@book{berry_1985,
  title = {Bandit Problems: Sequential Allocation of Experiments.},
  shorttitle = {Bandit Problems},
  author = {Berry, Donald A and Fristedt, Bert E},
  year = {1985},
  publisher = {{Chapman \& Hall}},
  isbn = {978-94-015-3713-1},
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  annotation = {OCLC: 922650712},
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}

@book{bertsekas_,
  title = {Introduction to {{Probability}}},
  author = {Bertsekas, Dimitri P and Tsitsiklis, John N},
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@book{bertsekas_2005,
  title = {Dynamic Programming and Optimal Control},
  author = {Bertsekas, Dimitri},
  year = {2005},
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}

@book{bertsekas_2005a,
  title = {Neurodynamic\_{{Programming}}},
  author = {Bertsekas, Dimitri},
  year = {2005},
  publisher = {{Athena Scientific}},
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}

@article{beskow_2010,
  title = {Developing a {{Simplified Consent Form}} for {{Biobanking}}},
  author = {Beskow, Laura M. and Friedman, Jo{\"e}lle Y. and Hardy, N. Chantelle and Lin, Li and Weinfurt, Kevin P.},
  editor = {Goodyear, Michael},
  year = {2010},
  month = oct,
  journal = {PLoS ONE},
  volume = {5},
  number = {10},
  pages = {e13302},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0013302},
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}

@book{bespalov_2020,
  title = {Good {{Research Practice}} in {{Non-Clinical Pharmacology}} and {{Biomedicine}}},
  editor = {Bespalov, Anton and Michel, Martin C. and Steckler, Thomas},
  year = {2020},
  series = {Handbook of {{Experimental Pharmacology}}},
  volume = {257},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-33656-1},
  isbn = {978-3-030-33655-4 978-3-030-33656-1},
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}

@article{bestmann_2008,
  title = {Influence of {{Uncertainty}} and {{Surprise}} on {{Human Corticospinal Excitability}} during {{Preparation}} for {{Action}}},
  author = {Bestmann, Sven and Harrison, Lee M. and Blankenburg, Felix and Mars, Rogier B. and Haggard, Patrick and Friston, Karl J. and Rothwell, John C.},
  year = {2008},
  month = may,
  journal = {Current Biology},
  volume = {18},
  number = {10},
  pages = {775--780},
  issn = {09609822},
  doi = {10.1016/j.cub.2008.04.051},
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}

@book{bewersdorff_2011,
  title = {{Statistik - wie und warum sie funktioniert: ein mathematisches Lesebuch}},
  shorttitle = {{Statistik - wie und warum sie funktioniert}},
  author = {Bewersdorff, J{\"o}rg},
  year = {2011},
  series = {{Studium}},
  edition = {1. Aufl},
  publisher = {{Vieweg + Teubner}},
  address = {{Wiesbaden}},
  abstract = {Das als eine Art Lesebuch konzipierte Werk bietet eine gut verst\"andliche und meistenteils nur solide Kenntnisse der Oberstufenmathematik voraussetzende Einf\"uhrung in die mathematische Statistik. Dabei werden die wesentlichen Begriffsbildungen, Sachverhalte und Methoden \"uberwiegend exemplarisch vermittelt und in Textform erkl\"art, die auf das Notwendige reduzierten formalen Grundlagen meist sukzessive aus dem Kontext heraus geliefert. Eine systematische und mathematisch stringente Darstellung liegt hier mithin nicht vor. Ausblicke auf mathematisch anspruchsvollere Sachverhalte sind dabei eigens gekennzeichnet und erm\"oglichen bei Bedarf eine etwas tiefer gehende Auseinandersetzung. Obgleich auch eine nicht sehr intensiv betriebene Lekt\"ure brauchbares Wissen vermittelt, bedarf es f\"ur ein solides Verst\"andnis der vorausgesetzten Mathematik. F\"ur interessierte Praktiker und Einsteiger, Lehrer und Studierende als eine 1. ann\"ahernde und einen soliden Einblick in die mathematische Statistik verschaffende und Orientierung bietende Lekt\"ure breit empfohlen, auch als Vorbereitung auf weitergehende Studien. (2 S)},
  isbn = {978-3-8348-1753-2},
  langid = {german},
  annotation = {OCLC: 750826926},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RUSWPA9U\\Bewersdorff - 2011 - Statistik - wie und warum sie funktioniert ein ma.pdf}
}

@article{bhat_,
  title = {On the Derivation of the {{Bayesian Information Criterion}}},
  author = {Bhat, H S and Kumar, N},
  pages = {5},
  abstract = {We present a careful derivation of the Bayesian Inference Criterion (BIC) for model selection. The BIC is viewed here as an approximation to the Bayes Factor. One of the main ingredients in the approximation, the use of Laplace's method for approximating integrals, is explained well in the literature. Our derivation sheds light on this and other steps in the derivation, such as the use of a flat prior and the invocation of the weak law of large numbers, that are not often discussed in detail.},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\29TTBT7S\\Bhat und Kumar - On the derivation of the Bayesian Information Crit.pdf}
}

@book{bhattacharya_2009,
  title = {Stochastic Processes with Applications},
  author = {Bhattacharya, R. N. and Waymire, Edward C.},
  year = {2009},
  series = {Classics in Applied Mathematics},
  edition = {SIAM ed., [Classics ed.]},
  number = {61},
  publisher = {{Society for Industrial and Applied Mathematics}},
  address = {{Philadelphia}},
  isbn = {978-0-89871-689-4},
  lccn = {QA274 .B49 2009},
  keywords = {Stochastic processes},
  annotation = {OCLC: ocn373561560},
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}

@article{bialek_2012,
  title = {Statistical Mechanics for Natural Flocks of Birds},
  author = {Bialek, W. and Cavagna, A. and Giardina, I. and Mora, T. and Silvestri, E. and Viale, M. and Walczak, A. M.},
  year = {2012},
  month = mar,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {109},
  number = {13},
  pages = {4786--4791},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1118633109},
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  annotation = {00305},
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}

@article{biasiucci_2019,
  title = {Electroencephalography},
  author = {Biasiucci, Andrea and Franceschiello, Benedetta and Murray, Micah M.},
  year = {2019},
  month = feb,
  journal = {Current Biology},
  volume = {29},
  number = {3},
  pages = {R80-R85},
  issn = {09609822},
  doi = {10.1016/j.cub.2018.11.052},
  langid = {english},
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}

@article{bielczyk_2019,
  ids = {bielczyk_disentangling_2019-1},
  title = {Disentangling Causal Webs in the Brain Using Functional Magnetic Resonance Imaging: {{A}} Review of Current Approaches},
  shorttitle = {Disentangling Causal Webs in the Brain Using Functional Magnetic Resonance Imaging},
  author = {Bielczyk, Natalia Z. and Uithol, Sebo and {van Mourik}, Tim and Anderson, Paul and Glennon, Jeffrey C. and Buitelaar, Jan K.},
  year = {2019},
  month = jan,
  journal = {Network Neuroscience},
  volume = {3},
  number = {2},
  pages = {237--273},
  issn = {2472-1751},
  doi = {10.1162/netn_a_00062},
  abstract = {In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.},
  langid = {english},
  annotation = {00003},
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}

@article{biessmann_,
  title = {Data-Driven Analysis for Multimodal Neuroimaging},
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}

@article{bigdely-shamlo_2015,
  title = {The {{PREP}} Pipeline: Standardized Preprocessing for Large-Scale {{EEG}} Analysis},
  shorttitle = {The {{PREP}} Pipeline},
  author = {{Bigdely-Shamlo}, Nima and Mullen, Tim and Kothe, Christian and Su, Kyung-Min and Robbins, Kay A.},
  year = {2015},
  month = jun,
  journal = {Frontiers in Neuroinformatics},
  volume = {9},
  issn = {1662-5196},
  doi = {10.3389/fninf.2015.00016},
  abstract = {The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.},
  langid = {english},
  annotation = {00073},
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}

@book{bijma_2017,
  title = {An {{Introduction}} to {{Mathematical Statistics}}},
  author = {Bijma, Fetsje and Jonker, Marianne and {van der Vaart}, Aad},
  year = {2017},
  month = may,
  publisher = {{Amsterdam University Press}},
  doi = {10.5117/9789462985100},
  isbn = {978-94-6298-510-0},
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}

@article{bijsterbosch_,
  title = {List of {{Primer Appendices}}},
  author = {Bijsterbosch, Janine and Smith, Stephen},
  pages = {35},
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}

@article{billheimer_2019,
  title = {Predictive {{Inference}} and {{Scientific Reproducibility}}},
  author = {Billheimer, Dean},
  year = {2019},
  month = mar,
  journal = {The American Statistician},
  volume = {73},
  number = {sup1},
  pages = {291--295},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2018.1518270},
  abstract = {Most statistical analyses use hypothesis tests or estimation about parameters to form inferential conclusions. I think this is noble, but misguided. The point of view expressed here is that observables are fundamental, and that the goal of statistical modeling should be to predict future observations, given the current data and other relevant information. Further, the prediction of future observables provides multiple advantages to practicing scientists, and to science in general. These include an interpretable numerical summary of a quantity of direct interest to current and future researchers, a calibrated prediction of what's likely to happen in future experiments, a prediction that can be either ``corroborated'' or ``refuted'' through experimentation, and avoidance of inference about parameters; quantities that exists only as convenient indices of hypothetical distributions. Finally, the predictive probability of a future observable can be used as a standard for communicating the reliability of the current work, regardless of whether confirmatory experiments are conducted. Adoption of this paradigm would improve our rigor for scientific accuracy and reproducibility by shifting our focus from ``finding differences'' among hypothetical parameters to predicting observable events based on our current scientific understanding.},
  langid = {english},
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}

@book{billingsley_1995,
  title = {Probability and Measure},
  author = {Billingsley, Patrick},
  year = {1995},
  series = {Wiley Series in Probability and Mathematical Statistics},
  edition = {3rd ed},
  publisher = {{Wiley}},
  address = {{New York}},
  isbn = {978-0-471-00710-4},
  lccn = {QA273 .B575 1995},
  keywords = {Measure theory,Probabilities},
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}

@article{bingham_2018,
  title = {Pyro: {{Deep Universal Probabilistic Programming}}},
  shorttitle = {Pyro},
  author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and Horsfall, Paul and Goodman, Noah D.},
  year = {2018},
  month = oct,
  journal = {arXiv:1810.09538 [cs, stat]},
  eprint = {1810.09538},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Computer Science - Programming Languages,Statistics - Machine Learning},
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}

@article{biscay_1996,
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@article{bischoff-grethe_2007,
  title = {A Technique for the Deidentification of Structural Brain {{MR}} Images},
  author = {{Bischoff-Grethe}, Amanda and Ozyurt, I. Burak and Busa, Evelina and Quinn, Brian T. and {Fennema-Notestine}, Christine and Clark, Camellia P. and Morris, Shaunna and Bondi, Mark W. and Jernigan, Terry L. and Dale, Anders M.},
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@book{bishop_2006,
  title = {Pattern Recognition and Machine Learning},
  author = {Bishop, Christopher M.},
  year = {2006},
  series = {Information Science and Statistics},
  publisher = {{Springer}},
  address = {{New York}},
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  lccn = {Q327 .B52 2006},
  keywords = {Machine learning,Pattern perception},
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}

@article{bishop_2013,
  title = {Model-Based Machine Learning},
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  number = {1984},
  pages = {20120222},
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}

@article{biswal_2010,
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  year = {2010},
  month = mar,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {107},
  number = {10},
  pages = {4734--4739},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0911855107},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2UW3RNRG\\Biswal et al. - 2010 - Toward discovery science of human brain function.pdf;C\:\\Users\\dirko\\Zotero\\storage\\D4F94E53\\Biswal et al. - 2010 - Toward discovery science of human brain function.pdf;C\:\\Users\\dirko\\Zotero\\storage\\KDNXFGHX\\Biswal, Mennes et al 2010 - Toward discovery science of human.pdf}
}

@article{bitzer_2014,
  title = {Perceptual Decision Making: Drift-Diffusion Model Is Equivalent to a {{Bayesian}} Model},
  shorttitle = {Perceptual Decision Making},
  author = {Bitzer, Sebastian and Park, Hame and Blankenburg, Felix and Kiebel, Stefan J.},
  year = {2014},
  journal = {Frontiers in Human Neuroscience},
  volume = {8},
  issn = {1662-5161},
  doi = {10.3389/fnhum.2014.00102},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WNUIKS2Z\\Bitzer et al_2014_Frontiers in Human Neuroscience_Perceptual decision making.pdf}
}

@article{bland_2012,
  title = {Different {{Varieties}} of {{Uncertainty}} in {{Human Decision-Making}}},
  author = {Bland, Amy R. and Schaefer, Alexandre},
  year = {2012},
  journal = {Frontiers in Neuroscience},
  volume = {6},
  issn = {1662-4548},
  doi = {10.3389/fnins.2012.00085},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PDE3I2GE\\Bland_Schaefer_2012_Frontiers in Neuroscience_Different Varieties of Uncertainty in Human Decision-Making.pdf}
}

@article{blankenburg_2006,
  title = {The {{Cutaneous Rabbit Illusion Affects Human Primary Sensory Cortex Somatotopically}}},
  author = {Blankenburg, Felix and Ruff, Christian C and Deichmann, Ralf and Rees, Geraint and Driver, Jon},
  editor = {Ashe, James},
  year = {2006},
  month = feb,
  journal = {PLoS Biology},
  volume = {4},
  number = {3},
  pages = {e69},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.0040069},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PL3GE78A\\Blankenburg et al_2006_PLoS Biology_The Cutaneous Rabbit Illusion Affects Human Primary Sensory Cortex.pdf}
}

@article{blei_2017,
  title = {Science and Data Science},
  author = {Blei, David M. and Smyth, Padhraic},
  year = {2017},
  month = aug,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {114},
  number = {33},
  pages = {8689--8692},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1702076114},
  abstract = {We presented a holistic view of data science, a view that has implications for practice, research, and education. It suggests the potential in integrating research that crosses the statistical, computational, and human boundaries. Furthermore, it puts into focus that, to solve real world problems, a data scientist will need to undertake tasks that are beyond their traditional training. Data science is more than the combination of statistics and computer science\textemdash it requires training in how to weave statistical and computational techniques into a larger framework, problem by problem, and to address discipline-specific questions. Holistic data science requires that we understand the context of data, appreciate the responsibilities involved in using private and public data, and clearly communicate what a dataset can and cannot tell us about the world.},
  langid = {english},
  annotation = {00028},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4Z4XT5YR\\Blei und Smyth - 2017 - Science and data science.pdf}
}

@article{blei_2017a,
  title = {Variational {{Inference}}: {{A Review}} for {{Statisticians}}},
  shorttitle = {Variational {{Inference}}},
  author = {Blei, David M. and Kucukelbir, Alp and McAuliffe, Jon D.},
  year = {2017},
  month = apr,
  journal = {Journal of the American Statistical Association},
  volume = {112},
  number = {518},
  pages = {859--877},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.2017.1285773},
  langid = {english},
  annotation = {00501},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GBRTGYQ9\\Blei et al. - 2017 - Variational Inference A Review for Statisticians.pdf}
}

@article{bloch_1946,
  title = {Nuclear {{Induction}}},
  author = {Bloch, F.},
  year = {1946},
  month = oct,
  journal = {Physical Review},
  volume = {70},
  number = {7-8},
  pages = {460--474},
  issn = {0031-899X},
  doi = {10.1103/PhysRev.70.460},
  langid = {english},
  annotation = {07612},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AG5T88Q9\\Bloch_1946_Physical Review_Nuclear Induction.pdf}
}

@article{bogacz_2006,
  title = {The Physics of Optimal Decision Making: {{A}} Formal Analysis of Models of Performance in Two-Alternative Forced-Choice Tasks.},
  shorttitle = {The Physics of Optimal Decision Making},
  author = {Bogacz, Rafal and Brown, Eric and Moehlis, Jeff and Holmes, Philip and Cohen, Jonathan D.},
  year = {2006},
  journal = {Psychological Review},
  volume = {113},
  number = {4},
  pages = {700--765},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/0033-295X.113.4.700},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9XPYARC4\\Bogacz et al_2006_Psychological Review_The physics of optimal decision making.pdf}
}

@article{bogacz_2007,
  title = {Optimal Decision-Making Theories: Linking Neurobiology with Behaviour},
  shorttitle = {Optimal Decision-Making Theories},
  author = {Bogacz, Rafal},
  year = {2007},
  month = mar,
  journal = {Trends in Cognitive Sciences},
  volume = {11},
  number = {3},
  pages = {118--125},
  issn = {13646613},
  doi = {10.1016/j.tics.2006.12.006},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YYLQLDF8\\Bogacz_2007_Trends in Cognitive Sciences_Optimal decision-making theories.pdf}
}

@article{bogacz_2010,
  title = {The Neural Basis of the Speed\textendash Accuracy Tradeoff},
  author = {Bogacz, Rafal and Wagenmakers, Eric-Jan and Forstmann, Birte U. and Nieuwenhuis, Sander},
  year = {2010},
  month = jan,
  journal = {Trends in Neurosciences},
  volume = {33},
  number = {1},
  pages = {10--16},
  issn = {01662236},
  doi = {10.1016/j.tins.2009.09.002},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HVFBDDVU\\Bogacz et al_2010_Trends in Neurosciences_The neural basis of the speed–accuracy tradeoff.pdf;C\:\\Users\\dirko\\Zotero\\storage\\MUDW3V7N\\Bogacz et al_2010_Trends in Neurosciences_The neural basis of the speed–accuracy tradeoff.pdf}
}

@article{bogacz_2017,
  title = {A Tutorial on the Free-Energy Framework for Modelling Perception and Learning},
  author = {Bogacz, Rafal},
  year = {2017},
  month = feb,
  journal = {Journal of Mathematical Psychology},
  volume = {76},
  pages = {198--211},
  issn = {00222496},
  doi = {10.1016/j.jmp.2015.11.003},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7RWZR8VE\\Bogacz - 2017 - A tutorial on the free-energy framework for modell.pdf;C\:\\Users\\dirko\\Zotero\\storage\\KV78HAQE\\Bogacz_2017_Journal of Mathematical Psychology_A tutorial on the free-energy framework for modelling perception and learning.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TPKNJIBC\\Bogacz_JMP_15.pdf}
}

@article{bojak_2010,
  title = {Connecting {{Mean Field Models}} of {{Neural Activity}} to {{EEG}} and {{fMRI Data}}},
  author = {Bojak, Ingo and Oostendorp, Thom F. and Reid, Andrew T. and K{\"o}tter, Rolf},
  year = {2010},
  month = jun,
  journal = {Brain Topography},
  volume = {23},
  number = {2},
  pages = {139--149},
  issn = {0896-0267, 1573-6792},
  doi = {10.1007/s10548-010-0140-3},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\W6CMPZEW\\Bojak et al_2010_Brain Topography_Connecting Mean Field Models of Neural Activity to EEG and fMRI Data.pdf}
}

@article{boland_2017,
  title = {Ten {{Simple Rules}} to {{Enable Multi-site Collaborations}} through {{Data Sharing}}},
  author = {Boland, Mary Regina and Karczewski, Konrad J. and Tatonetti, Nicholas P.},
  year = {2017},
  month = jan,
  journal = {PLOS Computational Biology},
  volume = {13},
  number = {1},
  pages = {e1005278},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1005278},
  langid = {english},
  annotation = {00020},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XBCLDM8V\\Boland et al. - 2017 - Ten Simple Rules to Enable Multi-site Collaboratio.pdf}
}

@article{boldt_2019,
  title = {Confidence Modulates Exploration and Exploitation in Value-Based Learning},
  author = {Boldt, Annika and Blundell, Charles and De Martino, Benedetto},
  year = {2019},
  month = jan,
  journal = {Neuroscience of Consciousness},
  volume = {2019},
  number = {1},
  pages = {niz004},
  issn = {2057-2107},
  doi = {10.1093/nc/niz004},
  abstract = {Uncertainty is ubiquitous in cognitive processing. In this study, we aim to investigate the ability agents possess to track and report the noise inherent in their mental operations, often in the form of confidence judgments. Here, we argue that humans can use uncertainty inherent in their representations of value beliefs to arbitrate between exploration and exploitation. Such uncertainty is reflected in explicit confidence judgments. Using a novel variant of a multi-armed bandit paradigm, we studied how beliefs were formed and how uncertainty in the encoding of these value beliefs (belief confidence) evolved over time. We found that people used uncertainty to arbitrate between exploration and exploitation, reflected in a higher tendency toward exploration when their confidence in their value representations was low. We furthermore found that value uncertainty can be linked to frameworks of metacognition in decision making in two ways. First, belief confidence drives decision confidence, i.e. people's evaluation of their own choices. Second, individuals with higher metacognitive insight into their choices were also better at tracing the uncertainty in their environment. Together, these findings argue that such uncertainty representations play a key role in the context of cognitive control.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\W3T5V4X3\\Boldt et al. - 2019 - Confidence modulates exploration and exploitation .pdf}
}

@article{boly_2011,
  title = {Preserved {{Feedforward But Impaired Top-Down Processes}} in the {{Vegetative State}}},
  author = {Boly, M. and Garrido, M. I. and Gosseries, O. and Bruno, M.-A. and Boveroux, P. and Schnakers, C. and Massimini, M. and Litvak, V. and Laureys, S. and Friston, K.},
  year = {2011},
  month = may,
  journal = {Science},
  volume = {332},
  number = {6031},
  pages = {858--862},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1202043},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WL4V6F4J\\Boly et al_2011_Science_Preserved Feedforward But Impaired Top-Down Processes in the Vegetative State.pdf}
}

@article{boly_2012,
  title = {Connectivity {{Changes Underlying Spectral EEG Changes}} during {{Propofol-Induced Loss}} of {{Consciousness}}},
  author = {Boly, M. and Moran, R. and Murphy, M. and Boveroux, P. and Bruno, M.-A. and Noirhomme, Q. and Ledoux, D. and Bonhomme, V. and Brichant, J.-F. and Tononi, G. and Laureys, S. and Friston, K.},
  year = {2012},
  month = may,
  journal = {Journal of Neuroscience},
  volume = {32},
  number = {20},
  pages = {7082--7090},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3769-11.2012},
  langid = {english},
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}

@article{bonacich_2007,
  title = {Some Unique Properties of Eigenvector Centrality},
  author = {Bonacich, Phillip},
  year = {2007},
  month = oct,
  journal = {Social Networks},
  volume = {29},
  number = {4},
  pages = {555--564},
  issn = {03788733},
  doi = {10.1016/j.socnet.2007.04.002},
  langid = {english},
  annotation = {00000},
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}

@article{bonmassar_2001,
  title = {Influence of {{EEG}} Electrodes on the {{BOLD fMRI}} Signal},
  author = {Bonmassar, Giorgio and Hadjikhani, Nouchine and Ives, John R. and Hinton, Denise and Belliveau, John W.},
  year = {2001},
  journal = {Human brain mapping},
  volume = {14},
  number = {2},
  pages = {108--115},
  annotation = {00076},
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}

@article{bonmassar_2001a,
  title = {Spatiotemporal {{Brain Imaging}} of {{Visual-Evoked Activity Using Interleaved EEG}} and {{fMRI Recordings}}},
  author = {Bonmassar, G. and Schwartz, D.P. and Liu, A.K. and Kwong, K.K. and Dale, A.M. and Belliveau, J.W.},
  year = {2001},
  month = jun,
  journal = {NeuroImage},
  volume = {13},
  number = {6},
  pages = {1035--1043},
  issn = {10538119},
  doi = {10.1006/nimg.2001.0754},
  langid = {english},
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}

@book{borenstein_2009,
  title = {Introduction to Meta-Analysis},
  editor = {Borenstein, Michael},
  year = {2009},
  publisher = {{John Wiley \& Sons}},
  address = {{Chichester, U.K}},
  abstract = {This text provides a concise and clearly presented discussion of all the elements in a meta-analysis. It is illustrated with worked examples throughout, with visual explanations, using screenshots from Excel spreadsheets and computer programs such as Comprehensive Meta-Analysis (CMA) or Strata},
  isbn = {978-0-470-05724-7},
  langid = {english},
  lccn = {R853.M48 I58 2009},
  keywords = {Meta-analysis,Meta-Analysis as Topic},
  annotation = {10966  OCLC: ocn263294996},
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}

@article{borenstein_2010,
  title = {A Basic Introduction to Fixed-Effect and Random-Effects Models for Meta-Analysis},
  author = {Borenstein, Michael and Hedges, Larry V. and Higgins, Julian P.T. and Rothstein, Hannah R.},
  year = {2010},
  month = apr,
  journal = {Research Synthesis Methods},
  volume = {1},
  number = {2},
  pages = {97--111},
  issn = {17592879},
  doi = {10.1002/jrsm.12},
  langid = {english},
  annotation = {01698},
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}

@article{borghi_2018,
  ids = {borghi_data_nodate,borghi_data_nodate-1},
  title = {Data Management and Sharing in Neuroimaging: {{Practices}} and Perceptions of {{MRI}} Researchers},
  shorttitle = {Data Management and Sharing in Neuroimaging},
  author = {Borghi, John A. and Van Gulick, Ana E.},
  year = {2018},
  month = jun,
  doi = {10.1101/266627},
  abstract = {Neuroimaging methods such as magnetic resonance imaging (MRI) involve complex data collection and analysis protocols, which necessitate the establishment of good research data management (RDM). Despite efforts within the field to address issues related to rigor and reproducibility, information about the RDM-related practices and perceptions of neuroimaging researchers remains largely anecdotal. To inform such efforts, we conducted an online survey of active MRI researchers that covered a range of RDM-related topics. Survey questions addressed the type(s) of data collected, tools used for data storage, organization, and analysis, and the degree to which practices are defined and standardized within a research group. Our results demonstrate that neuroimaging data is acquired in multifarious forms, transformed and analyzed using a wide variety of software tools, and that RDM practices and perceptions vary considerably both within and between research groups, with trainees reporting less consistency than faculty. Ratings of the maturity of RDM practices from ad-hoc to refined were relatively high during the data collection and analysis phases of a project and significantly lower during the data sharing phase. Perceptions of emerging practices including open access publishing and preregistration were largely positive, but demonstrated little adoption into current practice.},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2UDYTFHN\\Borghi und Gulick - Data management and sharing in neuroimaging Pract.pdf;C\:\\Users\\dirko\\Zotero\\storage\\EHBH8D4H\\Borghi and Van Gulick - 2018 - Data management and sharing in neuroimaging Pract.pdf;C\:\\Users\\dirko\\Zotero\\storage\\XDFCTVXF\\Borghi und Gulick - Data management and sharing in neuroimaging Pract.pdf;C\:\\Users\\dirko\\Zotero\\storage\\ZLCWBDV5\\Borghi and Gulick - Data management and sharing in neuroimaging Pract.pdf}
}

@article{bornkessel-schlesewsky_2019,
  title = {Toward a {{Neurobiologically Plausible Model}} of {{Language-Related}}, {{Negative Event-Related Potentials}}},
  author = {{Bornkessel-Schlesewsky}, Ina and Schlesewsky, Matthias},
  year = {2019},
  month = feb,
  journal = {Frontiers in Psychology},
  volume = {10},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2019.00298},
  abstract = {Language-related event-related potential (ERP) components such as the N400 have traditionally been associated with linguistic or cognitive functional interpretations. By contrast, it has been considerably more difficult to relate these components to neurobiologically grounded accounts of language. Here, we propose a theoretical framework based on a predictive coding architecture, within which negative language-related ERP components such as the N400 can be accounted for in a neurobiologically plausible manner. Specifically, we posit that the amplitude of negative language-related ERP components reflects precision-weighted prediction error signals, i.e., prediction errors weighted by the relevance of the information source leading to the error. From this perspective, precision has a direct link to cue validity in a particular language and, thereby, to relevance of individual linguistic features for internal model updating. We view components such as the N400 and LAN as members of a family with similar functional characteristics and suggest that latency and topography differences between these components reflect the locus of prediction errors and model updating within a hierarchically organized cortical predictive coding architecture. This account has the potential to unify findings from the full range of the N400 literature, including word-level, sentence-, and discourse-level results as well as cross-linguistic differences.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7YU3M87R\\Bornkessel-Schlesewsky und Schlesewsky - 2019 - Toward a Neurobiologically Plausible Model of Lang.pdf}
}

@article{borst_1999,
  title = {Information Theory and Neural Coding},
  author = {Borst, Alexander and Theunissen, Fr{\'e}d{\'e}ric E.},
  year = {1999},
  journal = {Nature neuroscience},
  volume = {2},
  number = {11},
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}

@book{bortz_2010,
  title = {{Statistik f\"ur Human- und Sozialwissenschaftler}},
  author = {Bortz, J{\"u}rgen and Schuster, Christof},
  year = {2010},
  series = {{Springer-Lehrbuch}},
  edition = {7., vollst\"andig \"uberarbeitete und erweiterte Auflage},
  publisher = {{Springer}},
  address = {{Berlin Heidelberg}},
  abstract = {Verlagsinfo: Der neue BORTZ-SCHUSTER rechnet (sich) f\"ur Sie! Denn er wurde in der 7. Auflage komplett \"uberarbeitet, erg\"anzt und didaktisch verbessert ; jetzt mit brandneuen Online-Zusatzmaterialien! An Bew\"ahrtem wurde festgehalten: Dieses Lehrbuch ist DIE Grundlage im Bachelorstudium, denn es ist alles Pr\"ufungsrelevante drin, von Elementarstatistik \"uber varianzanalytische Methoden bis zu multivariaten Methoden und \"Ubungsaufgaben helfen bei der Pr\"ufungsvorbereitung. Es ist weiterhin auch DAS Nachschlagewerk im Masterstudium und in der Forschung: Beispiele aus dem psychologischen Forschungsalltag helfen beim Nachvollziehen von Berechnungen und ein Glossar liefert schnell die wichtigsten Begriffsdefinitionen. NEUES wurde sinnvoll erg\"anzt, die Didaktik optimiert: Die neue Auflage ist neu strukturiert, k\"urzere Kapitel sorgen f\"ur einen klareren Aufbau. Die Grundlagen werden f\"ur Einsteiger verst\"andlicher und ausf\"uhrlicher dargestellt. Markierungen kennzeichnen Vertiefungskapitel. Hinweise zu EDV-Anwendungen sind systematisch in K\"asten hervorgehoben. Und besonders interessant f\"ur Anwender und die Pr\"ufungsvorbereitung: Eine neue begleitende Website enth\"alt Anleitungen f\"ur die Berechnung der im Buch enthaltenen Beispiele mit Statistikprogrammen (SPSS-Syntax) sowie Lerntools f\"ur Studierende und Materialien (Abbildungen und Folien) f\"ur Dozenten. Der BORTZ-SCHUSTER ist und bleibt ein unerl\"assliches Statistik-Lehrbuch f\"ur Studierende der Psychologie und Sozialwissenschaften, f\"ur Wissenschaftler und f\"ur Anwender},
  isbn = {978-3-642-12769-4},
  langid = {german},
  annotation = {OCLC: 845714518},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LIEK47CS\\Bortz_Schuster_2010_Statistik für Human- und Sozialwissenschaftler.pdf}
}

@article{boser_1992,
  title = {A {{Training Algorithm}} for {{Optimal Margin Classifiers}}},
  author = {Boser, Bernhard E and Guyon, Isabelle M and Vapnik, Vladimir N},
  year = {1992},
  pages = {9},
  abstract = {A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classi action functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.},
  langid = {english},
  annotation = {11421},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JQDZLKMQ\\Boser et al. - A Training Algorithm for Optimal Margin Classifier.pdf}
}

@article{boto_2018,
  title = {Moving Magnetoencephalography towards Real-World Applications with a Wearable System},
  author = {Boto, Elena and Holmes, Niall and Leggett, James and Roberts, Gillian and Shah, Vishal and Meyer, Sofie S. and Mu{\~n}oz, Leonardo Duque and Mullinger, Karen J. and Tierney, Tim M. and Bestmann, Sven and Barnes, Gareth R. and Bowtell, Richard and Brookes, Matthew J.},
  year = {2018},
  month = mar,
  journal = {Nature},
  issn = {1476-4687},
  doi = {10.1038/nature26147},
  abstract = {Imaging human brain function with techniques such as magnetoencephalography1 typically requires a subject to perform tasks while their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or to study processes in adults that require unconstrained head movement (such as spatial navigation). Here we describe a magnetoencephalography system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible owing to the integration of quantum sensors2,3, which do not rely on superconducting technology, with a system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution while subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Our results compare well to those of the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterization of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment and investigating the pathophysiology of movement disorders.},
  copyright = {2018 Nature Publishing Group},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\23RFYN8T\\Boto et al. - 2018 - Moving magnetoencephalography towards real-world a.pdf;C\:\\Users\\dirko\\Zotero\\storage\\7FFZ6XMD\\Boto et al_2018_Nature_Moving magnetoencephalography towards real-world applications with a wearable.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UZLLCUL2\\Boto et al. - 2018 - Moving magnetoencephalography towards real-world a.pdf;C\:\\Users\\dirko\\Zotero\\storage\\GL7MRKKX\\nature26147.html}
}

@article{botvinick_2012,
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  title = {Planning as Inference},
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@article{botvinick_2020,
  title = {Deep {{Reinforcement Learning}} and Its {{Neuroscientific Implications}}},
  author = {Botvinick, Matthew and Wang, Jane X. and Dabney, Will and Miller, Kevin J. and {Kurth-Nelson}, Zeb},
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  abstract = {The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. However, there is another area of recent AI work which has so far received less attention from neuroscientists, but which may have profound neuroscientific implications: deep reinforcement learning. Deep RL offers a comprehensive framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. In the present review, we provide a high-level introduction to deep RL, discuss some of its initial applications to neuroscience, and survey its wider implications for research on brain and behavior, concluding with a list of opportunities for next-stage research.},
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  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Quantitative Biology - Neurons and Cognition},
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@article{bowring_2019,
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  author = {Bowring, Alexander and Maumet, Camille and Nichols, Thomas E.},
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  journal = {Human Brain Mapping},
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  doi = {10.1002/hbm.24603},
  abstract = {A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this ``methodological plurality'' comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. In this work, our goal is to understand how choice of software package impacts on analysis results. We use publicly shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL, and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyse, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. Qualitatively we find similarities between packages, backed up by Neurosynth association analyses that correlate similar words and phrases to all three software package's unthresholded results for each of the studies we reanalyse. However, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 to 0.684 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our efforts to make this research reproducible.},
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}

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}

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@article{brockman_2016,
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  abstract = {OpenAI Gym1 is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.},
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@article{broomell_2019,
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  abstract = {Lee et al. (Computational Brain \& Behavior, 2019) discuss ways to improve research practices for evaluating quantitative cognitive models. We propose the additional research practices of careful consideration, documentation, and analysis of the stimuli used to generate responses. Current modeling practice too often fails to acknowledge how the stimuli used to generate responses from research participants can influence the results of model comparisons. We recommend researchers (a) disclose how the research stimuli were selected and (b) uncover and report the diagnosticity of the stimuli for the models being tested. To demonstrate the importance of this recommendation, we present lessons learned from model testing in judgment and decision-making research. We focus on the documentation and reporting of model-stimulus relationships, specifically diagnosticity, and demonstrate how transparent documentation of diagnosticity facilitates interpretation of the evidence it generates. We conclude with recommendations regarding research tools available to achieve these goals.},
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@book{brown_2006,
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  address = {{New York}},
  isbn = {978-1-59385-275-7 978-1-59385-274-0},
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@book{brown_2014,
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  publisher = {{John Wiley \& Sons, Inc}},
  address = {{Hoboken, New Jersey}},
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@article{brown_2017,
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@book{brunner_2013,
  title = {{Nichtparametrische Datenanalyse: Unverbundene Stichproben}},
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  author = {Brunner, Edgar and Munzel, Ullrich},
  year = {2013},
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  title = {Rank and {{Pseudo-Rank Procedures}} for {{Independent Observations}} in {{Factorial Designs}}: {{Using R}} and {{SAS}}},
  shorttitle = {Rank and {{Pseudo-Rank Procedures}} for {{Independent Observations}} in {{Factorial Designs}}},
  author = {Brunner, Edgar and Bathke, Arne C. and Konietschke, Frank},
  year = {2018},
  series = {Springer {{Series}} in {{Statistics}}},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-02914-2},
  isbn = {978-3-030-02912-8 978-3-030-02914-2},
  langid = {english},
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}

@article{buchel_1996,
  title = {Nonlinear Regression in Parametric Activation Studies},
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@article{buchel_1998,
  title = {Characterizing Stimulus\textendash Response Functions Using Nonlinear Regressors in Parametric {{fMRI}} Experiments},
  author = {B{\"u}chel, C. and Holmes, A. P. and Rees, G. and Friston, K. J.},
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@article{buchel_1998a,
  title = {Dynamic Changes in Effective Connectivity Characterized by Variable Parameter Regression and Kalman Filtering},
  author = {B{\"u}chel, Christian and Friston, K.J.},
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  abstract = {Attention to visual motion can increase the responsiveness of the motion-selective cortical area V5 and the posterior parietal cortex. We addressed attentional modulation of effective connectivity using variable parameter regression and functional magnetic resonance imaging. We present data from a single subject scanned under identical stimulus conditions (visual motion) while varying only the attentional component of the task. Variable parameter regression of the influence of V5 on PP revealed increased effective connectivity during attention to visual motion. With this dynamic measure of effective connectivity we were able to make inferences about the source of modulation by looking for regions that predicted the observed changes in connectivity. Using an ordinary regression analysis, we showed that activity in the prefrontal cortex could explain these changes and was sufficient to account for these modulatory influences on connections in the dorsal visual pathway. Hum. Brain Mapping 6:403\textendash 408, 1998. ௠ 1998 Wiley-Liss, Inc.},
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@article{buckley_2017,
  title = {The Free Energy Principle for Action and Perception: {{A}} Mathematical Review},
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  author = {Buckley, Christopher L. and Kim, Chang Sub and McGregor, Simon and Seth, Anil K.},
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  title = {Detection of Cortical Activation during Averaged Single Trials of a Cognitive Task Using Functional Magnetic Resonance Imaging},
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@article{budin-ljosne_2017,
  title = {Dynamic {{Consent}}: A Potential Solution to Some of the Challenges of Modern Biomedical Research},
  shorttitle = {Dynamic {{Consent}}},
  author = {{Budin-Lj{\o}sne}, Isabelle and Teare, Harriet J. A. and Kaye, Jane and Beck, Stephan and Bentzen, Heidi Beate and Caenazzo, Luciana and Collett, Clive and D'Abramo, Flavio and Felzmann, Heike and Finlay, Teresa and Javaid, Muhammad Kassim and Jones, Erica and Kati{\'c}, Vi{\v s}nja and Simpson, Amy and Mascalzoni, Deborah},
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  journal = {BMC Medical Ethics},
  volume = {18},
  number = {1},
  issn = {1472-6939},
  doi = {10.1186/s12910-016-0162-9},
  abstract = {Background: Innovations in technology have contributed to rapid changes in the way that modern biomedical research is carried out. Researchers are increasingly required to endorse adaptive and flexible approaches to accommodate these innovations and comply with ethical, legal and regulatory requirements. This paper explores how Dynamic Consent may provide solutions to address challenges encountered when researchers invite individuals to participate in research and follow them up over time in a continuously changing environment. Methods: An interdisciplinary workshop jointly organised by the University of Oxford and the COST Action CHIP ME gathered clinicians, researchers, ethicists, lawyers, research participants and patient representatives to discuss experiences of using Dynamic Consent, and how such use may facilitate the conduct of specific research tasks. The data collected during the workshop were analysed using a content analysis approach. Results: Dynamic Consent can provide practical, sustainable and future-proof solutions to challenges related to participant recruitment, the attainment of informed consent, participant retention and consent management, and may bring economic efficiencies. Conclusions: Dynamic Consent offers opportunities for ongoing communication between researchers and research participants that can positively impact research. Dynamic Consent supports inter-sector, cross-border approaches and large scale data-sharing. Whilst it is relatively easy to set up and maintain, its implementation will require that researchers re-consider their relationship with research participants and adopt new procedures.},
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}

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  abstract = {Functional Magnetic Resonance Imaging (fMRI) has become a standard tool for mapping the working brain's activation patterns, both in health and in disease. It is an interdisciplinary field and crosses the borders of neuroscience, psychology, psychiatry, radiology, mathematics, physics and engineering. Developments in techniques, procedures and our understanding of this field are expanding rapidly. In this second edition of this book provides an invaluable guide to how fMRI works, from introducing the basic ideas and principles to the underlying physics and physiology. He covers the relationship between fMRI and other imaging techniques and includes a guide to the statistical analysis of fMRI data.},
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  booktitle = {Spatial {{Statistics}}: {{Methodological Aspects}} and {{Applications}}},
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  abstract = {The goal of this short article is to summarize how random field theory has been used to test for activations in brain mapping applications. It is intended to be a general discussion and hence it is not very specific to individual applications. Tables of most widely used random fields, examples of their applications, as well as references to distributions of some of their relevant statistics are provided.},
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  pages = {289--300},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2012.07.004},
  abstract = {Replication of research findings is critical to the progress of scientific understanding. Accordingly, most scientific journals require authors to report experimental procedures in sufficient detail for independent researchers to replicate their work. To what extent do research reports in the functional neuroimaging literature live up to this standard? The present study evaluated methods reporting and methodological choices across 241 recent fMRI articles. Many studies did not report critical methodological details with regard to experimental design, data acquisition, and analysis. Further, many studies were underpowered to detect any but the largest statistical effects. Finally, data collection and analysis methods were highly flexible across studies, with nearly as many unique analysis pipelines as there were studies in the sample. Because the rate of false positive results is thought to increase with the flexibility of experimental designs, the field of functional neuroimaging may be particularly vulnerable to false positives. In sum, the present study documented significant gaps in methods reporting among fMRI studies. Improved methodological descriptions in research reports would yield significant benefits for the field.},
  keywords = {Analysis methods,Experimental design,fMRI,Methods reporting,Reproducibility,Statistical power},
  annotation = {00203},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\39PIP7R9\\Carp_2012_NeuroImage_The secret lives of experiments.pdf;C\:\\Users\\dirko\\Zotero\\storage\\DX93YUU4\\S1053811912007057.html}
}

@article{carpentier_,
  title = {The Optimism Principle in Various Sequential Decision Making Settings},
  author = {Carpentier, Alexandra},
  pages = {45},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\92GYL6IP\\Carpentier - The optimism principle in various sequential decis.pdf}
}

@book{carter_2000,
  title = {The {{Lebesgue-Stieltjes}} Integral: A Practical Introduction},
  shorttitle = {The {{Lebesgue-Stieltjes}} Integral},
  author = {Carter, M. and Van Brunt, B.},
  year = {2000},
  series = {Undergraduate Texts in Mathematics},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-95012-9},
  langid = {english},
  lccn = {QA312 .C37 2000},
  keywords = {Lebesgue integral},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5B3HYLJR\\Carter und Van Brunt - 2000 - The Lebesgue-Stieltjes integral a practical intro.pdf}
}

@article{carter_2016,
  title = {Thresholds, {{Power}}, and {{Sample Sizes}} in {{Clinical Neuroimaging}}},
  author = {Carter, Cameron S. and Lesh, Tyler A. and Barch, Deanna M.},
  year = {2016},
  month = mar,
  journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
  volume = {1},
  number = {2},
  pages = {99--100},
  issn = {24519022},
  doi = {10.1016/j.bpsc.2016.01.005},
  langid = {english},
  annotation = {00018},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JA4DSZK8\\Carter et al. - 2016 - Thresholds, Power, and Sample Sizes in Clinical Ne.pdf}
}

@article{casella_1992,
  title = {Explaining the {{Gibbs}} Sampler},
  author = {Casella, George and George, Edward I.},
  year = {1992},
  journal = {The American Statistician},
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}

@book{casella_2012,
  title = {Statistical {{Inference}}},
  author = {Casella, G and Berger, R},
  year = {2012},
  publisher = {{Duxbury}},
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}

@inproceedings{cassandra_1994,
  title = {Acting Optimally in Partially Observable Stochastic Domains},
  booktitle = {{{AAAI}}},
  author = {Cassandra, Anthony R. and Kaelbling, Leslie Pack and Littman, Michael L.},
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}

@article{caterini_,
  title = {Hamiltonian {{Variational Auto-Encoder}}},
  author = {Caterini, Anthony L and Doucet, Arnaud and Sejdinovic, Dino},
  pages = {11},
  abstract = {Variational Auto-Encoders (VAEs) have become very popular techniques to perform inference and learning in latent variable models: they allow us to leverage the rich representational power of neural networks to obtain flexible approximations of the posterior of latent variables as well as tight evidence lower bounds (ELBOs). Combined with stochastic variational inference, this provides a methodology scaling to large datasets. However, for this methodology to be practically efficient, it is necessary to obtain low-variance unbiased estimators of the ELBO and its gradients with respect to the parameters of interest. While the use of Markov chain Monte Carlo (MCMC) techniques such as Hamiltonian Monte Carlo (HMC) has been previously suggested to achieve this [25, 28], the proposed methods require specifying reverse kernels which have a large impact on performance. Additionally, the resulting unbiased estimator of the ELBO for most MCMC kernels is typically not amenable to the reparameterization trick. We show here how to optimally select reverse kernels in this setting and, by building upon Hamiltonian Importance Sampling (HIS) [19], we obtain a scheme that provides low-variance unbiased estimators of the ELBO and its gradients using the reparameterization trick. This allows us to develop a Hamiltonian Variational Auto-Encoder (HVAE). This method can be re-interpreted as a target-informed normalizing flow [22] which, within our context, only requires a few evaluations of the gradient of the sampled likelihood and trivial Jacobian calculations at each iteration.},
  langid = {english},
  annotation = {00003},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9D7YH39B\\Caterini et al. - Hamiltonian Variational Auto-Encoder.pdf}
}

@misc{chabot-leclerc_2017,
  title = {{{MATLAB}}\textregistered to {{Python}}: {{A Migration Guide}}},
  author = {{Chabot-Leclerc}, Alexandre},
  year = {2017},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\J66DGAHY\\Chabot-Leclerc - MATLAB®to Python A Migration Guide.pdf}
}

@article{chaiyotha_2017,
  title = {Uniform Random Walk on Unit Interval},
  author = {Chaiyotha, Kittisak and Jeeruphan, Thanawit},
  year = {2017},
  pages = {8},
  abstract = {We study the first-passage properties of a uniform random walk in the unit interval. Each step of a uniform random walk is uniformly distributed over [-a, na] where n is a natural number greater than 1 and a {$>$} 1. In this project, we found that the exit probability and mean exit time of uniform random walk are functions of the starting point.},
  annotation = {00000},
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}

@article{chakroun_2020,
  title = {Dopaminergic Modulation of the Exploration/Exploitation Trade-off in Human Decision-Making},
  author = {Chakroun, Karima and Mathar, David and Wiehler, Antonius and Ganzer, Florian and Peters, Jan},
  year = {2020},
  month = jun,
  journal = {eLife},
  volume = {9},
  pages = {e51260},
  issn = {2050-084X},
  doi = {10.7554/eLife.51260},
  abstract = {Involvement of dopamine in regulating exploration during decision-making has long been hypothesized, but direct causal evidence in humans is still lacking. Here, we use a combination of computational modeling, pharmacological intervention and functional magnetic resonance imaging to address this issue. Thirty-one healthy male participants performed a restless four-armed bandit task in a within-subjects design under three drug conditions: 150 mg of the dopamine precursor L-dopa, 2 mg of the D2 receptor antagonist haloperidol, and placebo. Choices were best explained by an extension of an established Bayesian learning model accounting for perseveration, directed exploration and random exploration. Modeling revealed attenuated directed exploration under L-dopa, while neural signatures of exploration, exploitation and prediction error were unaffected. Instead, L-dopa attenuated neural representations of overall uncertainty in insula and dorsal anterior cingulate cortex. Our results highlight the computational role of these regions in exploration and suggest that dopamine modulates how this circuit tracks accumulating uncertainty during decision-making.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8JE549WD\\Chakroun et al. - 2020 - Dopaminergic modulation of the explorationexploit.pdf}
}

@article{challinor_2010,
  title = {A Motion-Energy Model Predicts the Direction Discrimination and {{MAE}} Duration of Two-Stroke Apparent Motion at High and Low Retinal Illuminance},
  author = {Challinor, Kirsten L. and Mather, George},
  year = {2010},
  month = jun,
  journal = {Vision Research},
  volume = {50},
  number = {12},
  pages = {1109--1116},
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  doi = {10.1016/j.visres.2010.04.002},
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}

@article{chan_2016,
  title = {A {{Probability Distribution}} over {{Latent Causes}}, in the {{Orbitofrontal Cortex}}},
  author = {Chan, Stephanie C. Y. and Niv, Yael and Norman, Kenneth A.},
  year = {2016},
  journal = {The Journal of Neuroscience},
  volume = {36},
  number = {30},
  pages = {7817--7828},
  issn = {0270-6474},
  doi = {10.1523/JNEUROSCI.0659-16.2016},
  abstract = {UNLABELLED The orbitofrontal cortex (OFC) has been implicated in both the representation of "state," in studies of reinforcement learning and decision making, and also in the representation of "schemas," in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or "latent cause" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in the OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives, such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes. SIGNIFICANCE STATEMENT Our world is governed by hidden (latent) causes that we cannot observe, but which generate the observations we see. A range of high-level cognitive processes require inference of a probability distribution (or "belief distribution") over the possible latent causes that might be generating our current observations. This is true for reinforcement learning and decision making (where the latent cause comprises the true "state" of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or "schema"). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas.},
  pmid = {27466328},
  keywords = {a range of,bayes,belief distribution,but which generate the,by hidden,causes that we cannot,context,distribution,high-level cognitive processes require,inference of a probability,latent,observations we see,observe,or,our world is governed,over the possible latent,posterior distribution,rule,schemas,significance statement,state representation,ventromedial prefrontal cortex},
  annotation = {00000}
}

@article{chang_2011,
  title = {{{LIBSVM}}: {{A}} Library for Support Vector Machines},
  shorttitle = {{{LIBSVM}}},
  author = {Chang, Chih-Chung and Lin, Chih-Jen},
  year = {2011},
  month = apr,
  journal = {ACM Transactions on Intelligent Systems and Technology},
  volume = {2},
  number = {3},
  pages = {1--27},
  issn = {21576904},
  doi = {10.1145/1961189.1961199},
  abstract = {LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems, theoretical convergence, multi-class classification, probability estimates, and parameter selection are discussed in detail.},
  langid = {english},
  annotation = {36110},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NBKQ8QAG\\Chang and Lin - 2011 - LIBSVM A library for support vector machines.pdf}
}

@book{chang_2013,
  title = {R Graphics Cookbook},
  author = {Chang, Winston},
  year = {2013},
  publisher = {{O'Reilly}},
  address = {{Beijing Cambridge Farnham K\"oln Sebastopol Tokyo}},
  isbn = {978-1-4493-1695-2},
  langid = {english},
  annotation = {OCLC: 828794763},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\P3I7X3IH\\Chang - 2013 - R graphics cookbook.pdf}
}

@article{chappell_2009,
  title = {Variational {{Bayesian Inference}} for a {{Nonlinear Forward Model}}},
  author = {Chappell, M.A. and Groves, A.R. and Whitcher, B. and Woolrich, M.W.},
  year = {2009},
  month = jan,
  journal = {IEEE Transactions on Signal Processing},
  volume = {57},
  number = {1},
  pages = {223--236},
  issn = {1053-587X, 1941-0476},
  doi = {10.1109/TSP.2008.2005752},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ESY735TG\\Chappell et al_2009_IEEE Transactions on Signal Processing_Variational Bayesian Inference for a Nonlinear Forward Model.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PG8KH7RY\\Chappell et al. - 2009 - Variational Bayesian Inference for a Nonlinear For.pdf}
}

@article{chassang_2017,
  title = {The Impact of the {{EU}} General Data Protection Regulation on Scientific Research},
  author = {Chassang, Gauthier},
  year = {2017},
  month = jan,
  journal = {ecancermedicalscience},
  volume = {11},
  issn = {17546605},
  doi = {10.3332/ecancer.2017.709},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CRPY9HKP\\Chassang - 2017 - The impact of the EU general data protection regul.pdf}
}

@article{chatfield_2002,
  title = {{{CHAPMAN}} \& {{HALL}}/{{CRC}}},
  author = {Chatfield, Chris and Zidek, J.},
  year = {2002},
  annotation = {00002},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PKSP2SXM\\Carlin_Empirical_Bayes.pdf}
}

@article{chatzinikolaou_2016,
  title = {Data {{Policy}} and {{Data Sharing Agreement}} in the {{LifeWatchGreece Research Infrastructure}}},
  author = {Chatzinikolaou, Eva and Faulwetter, Sarah and Mavraki, Dimitra and Bourtzis, Tilemachos and Arvanitidis, Christos},
  year = {2016},
  month = nov,
  journal = {Biodiversity Data Journal},
  volume = {4},
  pages = {e10849},
  issn = {1314-2828, 1314-2836},
  doi = {10.3897/BDJ.4.e10849},
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}

@article{chau_2005,
  title = {The {{Talairach}} Coordinate of a Point in the {{MNI}} Space: How to Interpret It},
  shorttitle = {The {{Talairach}} Coordinate of a Point in the {{MNI}} Space},
  author = {Chau, Wilkin and McIntosh, Anthony R.},
  year = {2005},
  month = apr,
  journal = {NeuroImage},
  volume = {25},
  number = {2},
  pages = {408--416},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2004.12.007},
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}

@article{chawla_2000,
  title = {Relating {{Macroscopic Measures}} of {{Brain Activity}} to {{Fast}}, {{Dynamic Neuronal Interactions}}},
  author = {Chawla, D. and Lumer, E. D. and Friston, K. J.},
  year = {2000},
  month = dec,
  journal = {Neural Computation},
  volume = {12},
  number = {12},
  pages = {2805--2821},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/089976600300014737},
  langid = {english},
  annotation = {00050},
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}

@article{cheadle_2014,
  title = {Adaptive {{Gain Control}} during {{Human Perceptual Choice}}},
  author = {Cheadle, Samuel and Wyart, Valentin and Tsetsos, Konstantinos and Myers, Nicholas and {de~Gardelle}, Vincent and Herce~Casta{\~n}{\'o}n, Santiago and Summerfield, Christopher},
  year = {2014},
  month = mar,
  journal = {Neuron},
  volume = {81},
  number = {6},
  pages = {1429--1441},
  issn = {08966273},
  doi = {10.1016/j.neuron.2014.01.020},
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}

@article{chen_2008,
  title = {Dynamic Causal Modelling of Induced Responses},
  author = {Chen, C.C. and Kiebel, S.J. and Friston, K.J.},
  year = {2008},
  month = jul,
  journal = {NeuroImage},
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  number = {4},
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  doi = {10.1016/j.neuroimage.2008.03.026},
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}

@article{chen_2013,
  title = {Causal Interactions between Fronto-Parietal Central Executive and Default-Mode Networks in Humans},
  author = {Chen, A. C. and Oathes, D. J. and Chang, C. and Bradley, T. and Zhou, Z.-W. and Williams, L. M. and Glover, G. H. and Deisseroth, K. and Etkin, A.},
  year = {2013},
  month = dec,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {110},
  number = {49},
  pages = {19944--19949},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1311772110},
  langid = {english},
  annotation = {00158},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CVP2V889\\Chen et al. - 2013 - Causal interactions between fronto-parietal centra.pdf}
}

@article{chen_2017,
  title = {Is the Statistic Value All We Should Care about in Neuroimaging?},
  author = {Chen, Gang and Taylor, Paul A. and Cox, Robert W.},
  year = {2017},
  month = feb,
  journal = {NeuroImage},
  volume = {147},
  pages = {952--959},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2016.09.066},
  langid = {english},
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}

@article{cheng_2018,
  title = {Polynomial {{Regression As}} an {{Alternative}} to {{Neural Nets}}},
  author = {Cheng, Xi and Khomtchouk, Bohdan and Matloff, Norman and Mohanty, Pete},
  year = {2018},
  month = jun,
  journal = {arXiv:1806.06850 [cs, stat]},
  eprint = {1806.06850},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Despite the success of neural networks (NNs), there is still a concern among many over their ``black box'' nature. Why do they work? Here we present a simple analytic argument that NNs are in fact essentially polynomial regression models. This view will have various implications for NNs, e.g. providing an explanation for why convergence problems arise in NNs, and it gives rough guidance on avoiding overfitting. In addition, we use this phenomenon to predict and confirm a multicollinearity property of NNs not previously reported in the literature. Most importantly, given this loose correspondence, one may choose to routinely use polynomial models instead of NNs, thus avoiding some major problems of the latter, such as having to set many tuning parameters and dealing with convergence issues. We present a number of empirical results; in each case, the accuracy of the polynomial approach matches or exceeds that of NN approaches. A many-featured, open-source software package, polyreg, is available.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Learning,Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {00000},
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}

@book{chernoff_1989,
  title = {Sequential Analysis and Optimal Design},
  author = {Chernoff, Herman},
  year = {1989},
  series = {{{CBMS-NSF}} Regional Conference Series in Applied Mathematics},
  edition = {4. print},
  number = {8},
  publisher = {{Society for Industrial and Applied Mathematics}},
  address = {{Philadelphia, Pa}},
  isbn = {978-0-89871-006-9},
  langid = {english},
  annotation = {OCLC: 257884686},
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}

@article{choudhury_2014,
  title = {Big Data, Open Science and the Brain: Lessons Learned from Genomics},
  shorttitle = {Big Data, Open Science and the Brain},
  author = {Choudhury, Suparna and Fishman, Jennifer R. and McGowan, Michelle L. and Juengst, Eric T.},
  year = {2014},
  month = may,
  journal = {Frontiers in Human Neuroscience},
  volume = {8},
  issn = {1662-5161},
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}

@article{chow_2010,
  title = {Equivalence and {{Differences Between Structural Equation Modeling}} and {{State-Space Modeling Techniques}}},
  author = {Chow, Sy-Miin and Ho, Moon-ho R. and Hamaker, Ellen L. and Dolan, Conor V.},
  year = {2010},
  month = apr,
  journal = {Structural Equation Modeling: A Multidisciplinary Journal},
  volume = {17},
  number = {2},
  pages = {303--332},
  issn = {1070-5511, 1532-8007},
  doi = {10.1080/10705511003661553},
  langid = {english},
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}

@book{chowdhary_2020,
  title = {Fundamentals of {{Artificial Intelligence}}},
  author = {Chowdhary, K.R.},
  year = {2020},
  publisher = {{Springer India}},
  address = {{New Delhi}},
  doi = {10.1007/978-81-322-3972-7},
  isbn = {978-81-322-3970-3 978-81-322-3972-7},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YSHLCPY3\\Chowdhary - 2020 - Fundamentals of Artificial Intelligence.pdf}
}

@article{chowdhury_2013,
  title = {Dopamine Restores Reward Prediction Errors in Old Age},
  author = {Chowdhury, Rumana and {Guitart-Masip}, Marc and Lambert, Christian and Dayan, Peter and Huys, Quentin and D{\"u}zel, Emrah and Dolan, Raymond J},
  year = {2013},
  month = may,
  journal = {Nature Neuroscience},
  volume = {16},
  number = {5},
  pages = {648--653},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.3364},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5GJIZEED\\Chowdhury et al_2013_Nature Neuroscience_Dopamine restores reward prediction errors in old age.pdf}
}

@book{christakos_1992,
  title = {Random Field Models in Earth Sciences},
  author = {Christakos, George},
  year = {1992},
  publisher = {{Academic Press}},
  address = {{San Diego}},
  isbn = {978-0-12-174230-0},
  langid = {english},
  lccn = {QE33.2.M3 C487 1992},
  keywords = {Earth sciences,Mathematical models,Random fields,Stochastic processes},
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}

@book{christakos_2017,
  title = {Spatiotemporal Random Fields: Theory and Applications},
  shorttitle = {Spatiotemporal Random Fields},
  author = {Christakos, George},
  year = {2017},
  edition = {Second edition},
  publisher = {{Elsevier}},
  address = {{Amsterdam, Netherlands}},
  isbn = {978-0-12-803012-7},
  langid = {english},
  lccn = {RA566 .C58 2017},
  keywords = {Environmental health,Mathematical models,Stochastic processes},
  annotation = {00007},
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}

@book{christensen_2011,
  title = {Plane {{Answers}} to {{Complex Questions}}},
  author = {Christensen, Ronald},
  year = {2011},
  series = {Springer {{Texts}} in {{Statistics}}},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4419-9816-3},
  isbn = {978-1-4419-9815-6 978-1-4419-9816-3},
  langid = {english},
  annotation = {00879},
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}

@article{christophel_2018,
  title = {Cortical Specialization for Attended versus Unattended Working Memory},
  author = {Christophel, Thomas B. and Iamshchinina, Polina and Yan, Chang and Allefeld, Carsten and Haynes, John-Dylan},
  year = {2018},
  month = mar,
  journal = {Nature Neuroscience},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-018-0094-4},
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}

@article{christov_2004,
  title = {Real Time Electrocardiogram {{QRS}} Detection Using Combined Adaptive Threshold},
  author = {Christov, Ivaylo I},
  year = {2004},
  journal = {BioMedical Engineering OnLine},
  pages = {9},
  abstract = {Background: QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs Methods: A real-time detection method is proposed, based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold. The threshold combines three parameters: an adaptive slew-rate value, a second value which rises when high-frequency noise occurs, and a third one intended to avoid missing of low amplitude beats. Two algorithms were developed: Algorithm 1 detects at the current beat and Algorithm 2 has an RR interval analysis component in addition. The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number L of ECG leads, selfsynchronize to QRS or beat slopes and adapt to beat-to-beat intervals. Results: The algorithms were tested by an independent expert, thus excluding possible author's influence, using all 48 full-length ECG records of the MIT-BIH arrhythmia database. The results were: sensitivity Se = 99.69 \% and specificity Sp = 99.65 \% for Algorithm 1 and Se = 99.74 \% and Sp = 99.65 \% for Algorithm 2. Conclusion: The statistical indices are higher than, or comparable to those, cited in the scientific literature.},
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}

@article{chu_2011,
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  shorttitle = {Utilizing Temporal Information in {{fMRI}} Decoding},
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  year = {2011},
  month = sep,
  journal = {NeuroImage},
  volume = {58},
  number = {2},
  pages = {560--571},
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  doi = {10.1016/j.neuroimage.2011.06.053},
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}

@article{chumbley_2009,
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  year = {2009},
  month = jan,
  journal = {NeuroImage},
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  number = {1},
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  doi = {10.1016/j.neuroimage.2008.05.021},
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}

@article{chumbley_2010,
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  journal = {NeuroImage},
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@article{churchland_1988,
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  author = {Churchland, P. and Sejnowski, T.},
  year = {1988},
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  journal = {Science},
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  doi = {10.1126/science.3055294},
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@article{churchland_2007,
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  author = {Churchland, Mark M and Yu, Byron M and Sahani, Maneesh and Shenoy, Krishna V},
  year = {2007},
  month = oct,
  journal = {Current Opinion in Neurobiology},
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  number = {5},
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  doi = {10.1016/j.conb.2007.11.001},
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@article{cichy_2014,
  title = {Resolving Human Object Recognition in Space and Time},
  author = {Cichy, Radoslaw Martin and Pantazis, Dimitrios and Oliva, Aude},
  year = {2014},
  month = mar,
  journal = {Nature Neuroscience},
  volume = {17},
  number = {3},
  pages = {455--462},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.3635},
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}

@article{cichy_2020,
  title = {A {{M}}/{{EEG-fMRI Fusion Primer}}: {{Resolving Human Brain Responses}} in {{Space}} and {{Time}}},
  shorttitle = {A {{M}}/{{EEG-fMRI Fusion Primer}}},
  author = {Cichy, Radoslaw M. and Oliva, Aude},
  year = {2020},
  month = jul,
  journal = {Neuron},
  pages = {S0896627320305183},
  issn = {08966273},
  doi = {10.1016/j.neuron.2020.07.001},
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}

@article{cisek_2010,
  title = {Neural {{Mechanisms}} for {{Interacting}} with a {{World Full}} of {{Action Choices}}},
  author = {Cisek, Paul and Kalaska, John F.},
  year = {2010},
  month = jun,
  journal = {Annual Review of Neuroscience},
  volume = {33},
  number = {1},
  pages = {269--298},
  issn = {0147-006X, 1545-4126},
  doi = {10.1146/annurev.neuro.051508.135409},
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}

@article{cisek_2014,
  title = {On the Challenges and Mechanisms of Embodied Decisions},
  author = {Cisek, P. and {Pastor-Bernier}, A.},
  year = {2014},
  month = sep,
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {369},
  number = {1655},
  pages = {20130479--20130479},
  issn = {0962-8436, 1471-2970},
  doi = {10.1098/rstb.2013.0479},
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}

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  abstract = {AbstrackThis study investigated the cortical sources of the early (5\&250 ms) components of the pattern-onset visual evoked potential (VEP). VEPs were recorded in response to a small circular checkerboard stimulus that was flashed over a range of visual field positions. Temporally and spatially overlapping VEP components were distinguished by differences in retinotopic sensitivity and scalp topography, and by inverse dipole modeling. The C1 component (5Cr80 ms) was found to change its polarity and topography systematically as a function of stimulus position in a manner consistent with the retinotopic organization of the striate cortex. The P1 component (comprised of the P75 and PlO0 subcomponents) had a time course that overlapped the C1 but could be distinguished from the C1 by its differingtopography and reduced sensitivityto stimulus position. The P1 generators were localized to the lateral extrastriate cortex. Inverse dipole models were consistent with these striate and extrastriate source locations for the C1 and P1, respectively. The N1 component (120-180 ms) was found to originate from several spatially distinct generators that differed in their retinotopic organization. B 1995 Wiley-Liss, Inc.},
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  annotation = {00494},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YBF44XNB\\Clark et al. - 1994 - Identification of early visual evoked potential ge.pdf}
}

@article{cleveland_1979,
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  author = {Cleveland, William S.},
  year = {1979},
  month = dec,
  journal = {Journal of the American Statistical Association},
  volume = {74},
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}

@article{cogliatidezza_2017,
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  title = {Learning the Value of Information and Reward over Time When Solving Exploration-Exploitation Problems},
  author = {Cogliati Dezza, Irene and Yu, Angela J. and Cleeremans, Axel and Alexander, William},
  year = {2017},
  month = dec,
  journal = {Scientific Reports},
  volume = {7},
  number = {1},
  pages = {16919},
  publisher = {{Nature Publishing Group}},
  issn = {2045-2322},
  doi = {10.1038/s41598-017-17237-w},
  langid = {english},
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}

@article{cohen_1968,
  title = {Multiple Regression as a General Data-Analytic System.},
  author = {Cohen, Jacob},
  year = {1968},
  journal = {Psychological Bulletin},
  volume = {70},
  number = {6, Pt.1},
  pages = {426--443},
  issn = {1939-1455, 0033-2909},
  doi = {10.1037/h0026714},
  langid = {english},
  annotation = {01473},
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}

@book{cohen_1988,
  title = {Statistical Power Analysis for the Behavioral Sciences},
  author = {Cohen, Jacob},
  year = {1988},
  edition = {2nd ed},
  publisher = {{L. Erlbaum Associates}},
  address = {{Hillsdale, N.J}},
  isbn = {978-0-8058-0283-2},
  lccn = {HA29 .C66 1988},
  keywords = {Probabilities,Social sciences,Statistical methods,Statistical power analysis},
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}

@article{cohen_2007,
  ids = {cohen2007,cohen_should_2007-1},
  title = {Should {{I}} Stay or Should {{I}} Go? {{How}} the Human Brain Manages the Trade-off between Exploitation and Exploration},
  author = {Cohen, J. D and McClure, S. M and Yu, A. J},
  year = {2007},
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {362},
  number = {1481},
  pages = {933--942},
  publisher = {{The Royal Society London}},
  issn = {0962-8436},
  doi = {10.1098/rstb.2007.2098},
  abstract = {Many large and small decisions we make in our daily lives-which ice cream to choose, what research projects to pursue, which partner to marry-require an exploration of alternatives before committing to and exploiting the benefits of a particular choice. Furthermore, many decisions require re-evaluation, and further exploration of alternatives, in the face of changing needs or circumstances. That is, often our decisions depend on a higher level choice: whether to exploit well known but possibly suboptimal alternatives or to explore risky but potentially more profitable ones. How adaptive agents choose between exploitation and exploration remains an important and open question that has received relatively limited attention in the behavioural and brain sciences. The choice could depend on a number of factors, including the familiarity of the environment, how quickly the environment is likely to change and the relative value of exploiting known sources of reward versus the cost of reducing uncertainty through exploration. There is no known generally optimal solution to the exploration versus exploitation problem, and a solution to the general case may indeed not be possible. However, there have been formal analyses of the optimal policy under constrained circumstances. There have also been specific suggestions of how humans and animals may respond to this problem under particular experimental conditions as well as proposals about the brain mechanisms involved. Here, we provide a brief review of this work, discuss how exploration and exploitation may be mediated in the brain and highlight some promising future directions for research.},
  pmid = {17395573},
  keywords = {decision making,exploration,learning,neurotransmitters,prefrontal cortex,uncertainty},
  annotation = {00000},
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}

@book{cohen_2014,
  title = {Analyzing Neural Time Series Data: Theory and Practice},
  shorttitle = {Analyzing Neural Time Series Data},
  author = {Cohen, Mike X.},
  year = {2014},
  series = {Issues in Clinical and Cognitive Neuropsychology},
  publisher = {{The MIT Press}},
  address = {{Cambridge, Massachusetts}},
  isbn = {978-0-262-01987-3},
  langid = {english},
  lccn = {QP363.3 .C633 2014},
  keywords = {Artificial intelligence,Biological applications,Computational neuroscience,Neural networks (Computer science),Neural networks (Neurobiology)},
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}

@article{cohen_2017,
  title = {Computational Approaches to {{fMRI}} Analysis},
  author = {Cohen, Jonathan D. and Daw, Nathaniel and Engelhardt, Barbara and Hasson, Uri and Li, Kai and Niv, Yael and Norman, Kenneth A. and Pillow, Jonathan and Ramadge, Peter J. and {Turk-Browne}, Nicholas B. and Willke, Theodore L.},
  year = {2017},
  month = mar,
  journal = {Nature Neuroscience},
  volume = {20},
  number = {3},
  pages = {304--313},
  issn = {1546-1726},
  doi = {10.1038/nn.4499},
  abstract = {Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex\textemdash and distinctly human\textemdash signals in the brain: acts of cognition such as thoughts, intentions and memories.},
  copyright = {2017 Nature Publishing Group},
  langid = {english},
  annotation = {00014},
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}

@article{coil_2017,
  title = {Gut {{Check}}: {{The}} Evolution of an Educational Board Game},
  shorttitle = {Gut {{Check}}},
  author = {Coil, David A. and Ettinger, Cassandra L. and Eisen, Jonathan A.},
  year = {2017},
  month = apr,
  journal = {PLOS Biology},
  volume = {15},
  number = {4},
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  doi = {10.1371/journal.pbio.2001984},
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}

@article{cole_2013,
  title = {Multi-Task Connectivity Reveals Flexible Hubs for Adaptive Task Control},
  author = {Cole, Michael W and Reynolds, Jeremy R and Power, Jonathan D and Repovs, Grega and Anticevic, Alan and Braver, Todd S},
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  month = sep,
  journal = {Nature Neuroscience},
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  number = {9},
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  doi = {10.1038/nn.3470},
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}

@article{colebatch_2007,
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  author = {Colebatch, James G.},
  year = {2007},
  month = apr,
  journal = {Movement Disorders},
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}

@book{collins_2006,
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  author = {Collins, Peter J.},
  year = {2006},
  publisher = {{Oxford University Press}},
  address = {{Oxford}},
  isbn = {978-0-19-853382-5 978-0-19-929789-4},
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@article{colquhoun_,
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  title = {The Reproducibility of Research and the Misinterpretation of P-Values},
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}

@article{colquhoun_2019,
  title = {The {{False Positive Risk}}: {{A Proposal Concerning What}} to {{Do About}} {\emph{p}} -{{Values}}},
  shorttitle = {The {{False Positive Risk}}},
  author = {Colquhoun, David},
  year = {2019},
  month = mar,
  journal = {The American Statistician},
  volume = {73},
  number = {sup1},
  pages = {192--201},
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  doi = {10.1080/00031305.2018.1529622},
  abstract = {It is widely acknowledged that the biomedical literature suffers from a surfeit of false positive results. Part of the reason for this is the persistence of the myth that observation of p {$<$} 0.05 is sufficient justification to claim that you have made a discovery. It is hopeless to expect users to change their reliance on p-values unless they are offered an alternative way of judging the reliability of their conclusions. If the alternative method is to have a chance of being adopted widely, it will have to be easy to understand and to calculate. One such proposal is based on calculation of false positive risk(FPR). It is suggested that p-values and confidence intervals should continue to be given, but that they should be supplemented by a single additional number that conveys the strength of the evidence better than the p-value. This number could be the minimum FPR (that calculated on the assumption of a prior probability of 0.5, the largest value that can be assumed in the absence of hard prior data). Alternatively one could specify the prior probability that it would be necessary to believe in order to achieve an FPR of, say, 0.05.},
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}

@book{committeeontowardanopenscienceenterprise_2018,
  title = {Open {{Science}} by {{Design}}: {{Realizing}} a {{Vision}} for 21st {{Century Research}}},
  shorttitle = {Open {{Science}} by {{Design}}},
  author = {{Committee on Toward an Open Science Enterprise} and {Board on Research Data and Information} and {Policy and Global Affairs} and {National Academies of Sciences, Engineering, and Medicine}},
  year = {2018},
  month = aug,
  publisher = {{National Academies Press}},
  address = {{Washington, D.C.}},
  doi = {10.17226/25116},
  isbn = {978-0-309-47624-9},
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  abstract = {The independent component analysis (ICA) of a random vector consists of searching for a linear transformation that minimizes the statistical dependence between its components. In order to define suitable search criteria, the expansion of mutual information is utilized as a function of cumulants of increasing orders. An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time. The concept oflCA may actually be seen as an extension of the principal component analysis (PCA), which can only impose independence up to the second order and, consequently, defines directions that are orthogonal. Potential applications of ICA include data analysis and compression, Bayesian detection, localization of sources, and blind identification and deconvolution.},
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@article{conradt_2012,
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}

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}

@article{cooley_1965,
  title = {An Algorithm for the Machine Calculation of Complex {{Fourier}} Series},
  author = {Cooley, James W. and Tukey, John W.},
  year = {1965},
  month = may,
  journal = {Mathematics of Computation},
  volume = {19},
  number = {90},
  pages = {297--297},
  issn = {0025-5718},
  doi = {10.1090/S0025-5718-1965-0178586-1},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9RKAUPRB\\Cooley and Tukey - 1965 - An algorithm for the machine calculation of comple.pdf}
}

@article{coombes_,
  title = {Next Generation Neural Mass Models},
  author = {Coombes, Stephen},
  pages = {14},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DVRVSBNZ\\Coombes - Next generation neural mass models.pdf;C\:\\Users\\dirko\\Zotero\\storage\\GPZQ5T5W\\Coombes - Next generation neural mass models.pdf}
}

@article{coombes_2010,
  title = {Large-Scale Neural Dynamics: {{Simple}} and Complex},
  shorttitle = {Large-Scale Neural Dynamics},
  author = {Coombes, S.},
  year = {2010},
  month = sep,
  journal = {NeuroImage},
  volume = {52},
  number = {3},
  pages = {731--739},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2010.01.045},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5EJPRRX7\\Coombes_2010_NeuroImage_Large-scale neural dynamics.pdf}
}

@book{coombes_2014,
  title = {Neural {{Fields}}},
  editor = {Coombes, Stephen and {beim Graben}, Peter and Potthast, Roland and Wright, James},
  year = {2014},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-54593-1},
  isbn = {978-3-642-54592-4 978-3-642-54593-1},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4BYFZKBM\\Coombes et al_2014_Neural Fields.pdf}
}

@book{cooper_2009,
  title = {The Handbook of Research Synthesis and Meta-Analysis},
  editor = {Cooper, Harris M. and Hedges, Larry V. and Valentine, Jeff C.},
  year = {2009},
  edition = {2nd ed},
  publisher = {{Russell Sage Foundation}},
  address = {{New York}},
  isbn = {978-0-87154-163-5},
  langid = {english},
  lccn = {Q180.55.M4 H35 2009},
  keywords = {Handbooks; manuals; etc,Information storage and retrieval systems,Methodology,Research,Statistical methods},
  annotation = {02062  OCLC: ocn264670503},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\D7T55JUM\\Cooper et al. - 2009 - The handbook of research synthesis and meta-analys.pdf}
}

@book{cooper_2020,
  title = {Reporting Quantitative Research in Psychology: {{How}} to Meet {{APA Style Journal Article Reporting Standards}} (2nd Ed.).},
  shorttitle = {Reporting Quantitative Research in Psychology},
  author = {Cooper, Harris},
  year = {2020},
  publisher = {{American Psychological Association}},
  address = {{Washington}},
  doi = {10.1037/0000178-000},
  isbn = {978-1-4338-3283-3 978-1-4338-3342-7},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WE5JZFE9\\Cooper - 2020 - Reporting quantitative research in psychology How.pdf}
}

@article{corbetta_2002,
  title = {Control of Goal-Directed and Stimulus-Driven Attention in the Brain},
  author = {Corbetta, Maurizio and Shulman, Gordon L.},
  year = {2002},
  month = mar,
  journal = {Nature Reviews Neuroscience},
  volume = {3},
  number = {3},
  pages = {201--215},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/nrn755},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\IWMFB8SQ\\Corbetta_Shulman_2002_Nature Reviews Neuroscience_Control of goal-directed and stimulus-driven attention in the brain.pdf}
}

@article{corlett_2020,
  title = {Predicting to {{Perceive}} and {{Learning When}} to {{Learn}}},
  author = {Corlett, Philip},
  year = {2020},
  month = apr,
  journal = {Trends in Cognitive Sciences},
  volume = {24},
  number = {4},
  pages = {259--260},
  issn = {13646613},
  doi = {10.1016/j.tics.2019.12.005},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4YKBR5JV\\Corlett - 2020 - Predicting to Perceive and Learning When to Learn.pdf}
}

@article{cortes_1995,
  title = {Support-Vector Networks},
  author = {Cortes, Corinna and Vapnik, Vladimir},
  year = {1995},
  month = sep,
  journal = {Machine Learning},
  volume = {20},
  number = {3},
  pages = {273--297},
  issn = {0885-6125, 1573-0565},
  doi = {10.1007/BF00994018},
  abstract = {The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.},
  langid = {english},
  annotation = {38199},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XC7QSUCC\\Cortes and Vapnik - 1995 - Support-vector networks.pdf}
}

@book{cotton_2013,
  title = {Learning {{R}}},
  author = {Cotton, Richard},
  year = {2013},
  edition = {First Edition},
  publisher = {{O'Reilly}},
  address = {{Beijing ; Sebastopol, CA}},
  isbn = {978-1-4493-5710-8},
  lccn = {QA276.45.R3 C68 2013},
  keywords = {Data processing,R (Computer program language),Statistics},
  annotation = {OCLC: ocn830813799},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\I5KCTQIH\\Cotton - 2013 - Learning R.pdf}
}

@book{cover_2006,
  title = {Elements of Information Theory},
  author = {Cover, T. M. and Thomas, Joy A.},
  year = {2006},
  edition = {2nd ed},
  publisher = {{Wiley-Interscience}},
  address = {{Hoboken, N.J}},
  isbn = {978-0-471-24195-9},
  lccn = {Q360 .C68 2006},
  keywords = {Information theory},
  annotation = {OCLC: ocm59879802},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\C9PY6TQA\\Cover_Thomas_2006_Elements of information theory.pdf}
}

@book{cowan_1997,
  title = {Nuclear {{Magnetic Resonance}} and {{Relaxation}}},
  author = {Cowan, Brian},
  year = {1997},
  annotation = {00334},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\S4CVNVKA\\Brian Cowan - Nuclear Magnetic Resonance and Relaxation (1997).pdf}
}

@book{cowell_1999,
  title = {Probabilistic Networks and Expert Systems},
  editor = {Cowell, Robert G.},
  year = {1999},
  series = {Statistics for Engineering and Information Science},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-98767-5},
  langid = {english},
  lccn = {QA76.76.E95 P755 1999},
  keywords = {Expert systems (Computer science),Probabilities},
  annotation = {02953},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\F86X8XC2\\Cowell - 1999 - Probabilistic networks and expert systems.pdf}
}

@book{cox_1965,
  title = {The {{Theory}} of {{Stochastic Processes}}},
  author = {Cox, D.R. and Miller, H.D},
  year = {1965},
  annotation = {00710},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9IQ8V5G9\\Cox_Miller_1965_The Theory of Stochastic Processes.pdf}
}

@article{cox_2003,
  title = {Functional Magnetic Resonance Imaging ({{fMRI}}) ``Brain Reading'': Detecting and Classifying Distributed Patterns of {{fMRI}} Activity in Human Visual Cortex},
  shorttitle = {Functional Magnetic Resonance Imaging ({{fMRI}}) ``Brain Reading''},
  author = {Cox, David D and Savoy, Robert L},
  year = {2003},
  month = jun,
  journal = {NeuroImage},
  volume = {19},
  number = {2},
  pages = {261--270},
  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00049-1},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QZX2NRBI\\Cox_Savoy_2003_NeuroImage_Functional magnetic resonance imaging (fMRI) “brain reading”.pdf}
}

@article{cox_2017,
  title = {{{fMRI}} Clustering and False-Positive Rates},
  author = {Cox, Robert W. and Chen, Gang and Glen, Daniel R. and Reynolds, Richard C. and Taylor, Paul A.},
  year = {2017},
  month = apr,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {114},
  number = {17},
  pages = {E3370-E3371},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1614961114},
  abstract = {Recently, Eklund et al. (1) analyzed clustering methods in standard fMRI packages: AFNI (which we maintain), FSL, and SPM. They claim that ( i ) false-positive rates (FPRs) in traditional approaches are greatly inflated, questioning the validity of ``countless published fMRI studies''; ( ii ) nonparametric methods produce valid, but slightly conservative, FPRs; ( iii ) a common flawed assumption is that the spatial autocorrelation function (ACF) of fMRI noise is Gaussian-shaped; and ( iv ) a 15-y-old bug in AFNI's 3dClustSim significantly contributed to producing ``particularly high'' FPRs compared with other software. We repeated simulations from ref. 1 [Beijing\_Zang data (2), cf. ref. 3) and comment on each point briefly. Fig. 1 A \textendash{} D compares results of the ``buggy'' and ``fixed'' 3dClustSim. For each simulation, the typical difference was small: {$<$}mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"{$><$}mml:mrow{$><$}mml:mi mathvariant="normal"{$>\Delta<$}/mml:mi{$><$}mml:mtext{$>$}FPR{$<$}/mml:mtext{$><$}mml:mo{$>\lessequivlnt<$}/mml:mo{$><$}mml:mn{$>$}3{$<$}/mml:mn{$><$}mml:mo{$>-<$}/mml:mo{$><$}mml:mn{$>$}5{$<$}/mml:mn{$><$}mml:mo{$>\%<$}/mml:mo{$><$}/mml:mrow{$><$}/mml:math{$>$} at per-voxel {$<$}mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"{$><$}mml:mrow{$><$}mml:mi{$>$}P{$<$}/mml:mi{$><$}mml:mo{$>$}={$<$}/mml:mo{$><$}mml:mn{$>$}0.01{$<$}/mml:mn{$><$}/mml:mrow{$><$}/mml:math{$>$} and {$<$}mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"{$><$}mml:mrow{$><$}mml:mo{$>\lessequivlnt<$}/mml:mo{$><$}mml:mn{$>$}1{$<$}/mml:mn{$><$}mml:mo{$>-<$}/mml:mo{$><$}mml:mn{$>$}2{$<$}/mml:mn{$><$}mml:mo{$>\%<$}/mml:mo{$><$}/mml:mrow{$><$}/mml:math{$>$} for {$<$}mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"{$><$}mml:mrow{$><$}mml:mi{$>$}P{$<$}/mml:mi{$><$}mml:mo{$>$}={$<$}/mml:mo{$><$}mml:mn{$>$}0.001{$<$}/mml:mn{$><$}/mml:mrow{$><$}/mml:math{$>$}. The bug had only a minor impact. Fig. 1.  FPRs for various software scenarios, with 1,000 two-sample one-sided {$<$}mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"{$><$}mml:mi{$>$}t{$<$}/mml:mi{$><$}/mml:math{$>$}-tests (as in ref. \ldots{}  [{$\carriagereturn$}][1]1To whom correspondence should be addressed. Email: robertcox\{at\}mail.nih.gov.  [1]: \#xref-corresp-1-1},
  copyright = {\textcopyright{}  . http://www.pnas.org/site/misc/userlicense.xhtml},
  langid = {english},
  pmid = {28420798},
  annotation = {00035},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UW298UDS\\Cox et al_2017_Proceedings of the National Academy of Sciences_fMRI clustering and false-positive rates.pdf;C\:\\Users\\dirko\\Zotero\\storage\\RZLWBACX\\E3370.html}
}

@article{crain_1979,
  title = {Estimating the Parameters of a Truncated Normal Distribution},
  author = {Crain, Bradford R.},
  year = {1979},
  month = mar,
  journal = {Applied Mathematics and Computation},
  volume = {5},
  number = {2},
  pages = {149--156},
  issn = {00963003},
  doi = {10.1016/0096-3003(79)90004-3},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YNM2C3MI\\Crain - 1979 - Estimating the parameters of a truncated normal di.pdf}
}

@article{crottaz-herbette_2006,
  title = {Where and {{When}} the {{Anterior Cingulate Cortex Modulates Attentional Response}}: {{Combined fMRI}} and {{ERP Evidence}}},
  shorttitle = {Where and {{When}} the {{Anterior Cingulate Cortex Modulates Attentional Response}}},
  author = {{Crottaz-Herbette}, S. and Menon, V.},
  year = {2006},
  month = may,
  journal = {Journal of Cognitive Neuroscience},
  volume = {18},
  number = {5},
  pages = {766--780},
  issn = {0898-929X, 1530-8898},
  doi = {10.1162/jocn.2006.18.5.766},
  langid = {english},
  annotation = {00303},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\I7SMK6RY\\Crottaz-Herbette and Menon - 2006 - Where and When the Anterior Cingulate Cortex Modul.pdf}
}

@article{csaji_,
  title = {Approximation with {{Artificial Neural Networks}}},
  author = {Cs{\'a}ji, Bal{\'a}zs Csan{\'a}d},
  pages = {45},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9ARBS6T7\\Csáji - Approximation with Artificial Neural Networks.pdf}
}

@article{cune_,
  title = {{{ON THE VAN DER WAALS EQUATION}}},
  author = {CUNE, L C and APOSTOL, M},
  pages = {4},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8W2DGDD5\\CUNE and APOSTOL - ON THE VAN DER WAALS EQUATION.pdf}
}

@article{cybenko_1989,
  title = {Approximation by Superpositions of a Sigmoidal Function},
  author = {Cybenko, G},
  year = {1989},
  journal = {Mathematics of Control, Signals, and Systems},
  pages = {12},
  langid = {english},
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}

@book{czado_2011,
  title = {{Mathematische Statistik}},
  author = {Czado, Claudia and Schmidt, Thorsten},
  year = {2011},
  series = {{Statistik und ihre Anwendungen}},
  publisher = {{Springer}},
  address = {{Berlin}},
  isbn = {978-3-642-17260-1 978-3-642-17261-8},
  langid = {german},
  annotation = {00027  OCLC: 741722845},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YWUSURA4\\Czado_Schmidt_2011_Mathematische Statistik.pdf}
}

@article{dacosta_2020,
  title = {Active Inference on Discrete State-Spaces: {{A}} Synthesis},
  shorttitle = {Active Inference on Discrete State-Spaces},
  author = {Da Costa, Lancelot and Parr, Thomas and Sajid, Noor and Veselic, Sebastijan and Neacsu, Victorita and Friston, Karl},
  year = {2020},
  month = dec,
  journal = {Journal of Mathematical Psychology},
  volume = {99},
  pages = {102447},
  issn = {00222496},
  doi = {10.1016/j.jmp.2020.102447},
  abstract = {Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associated process theory has grown to incorporate complex generative models, enabling simulation of a wide range of complex behaviours. Due to successive developments in active inference, it is often difficult to see how its underlying principle relates to process theories and practical implementation. In this paper, we try to bridge this gap by providing a complete mathematical synthesis of active inference on discrete statespace models. This technical summary provides an overview of the theory, derives neuronal dynamics from first principles and relates this dynamics to biological processes. Furthermore, this paper provides a fundamental building block needed to understand active inference for mixed generative models; allowing continuous sensations to inform discrete representations. This paper may be used as follows: to guide research towards outstanding challenges, a practical guide on how to implement active inference to simulate experimental behaviour, or a pointer towards various in-silico neurophysiological responses that may be used to make empirical predictions.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6QHQZB82\\Da Costa et al. - 2020 - Active inference on discrete state-spaces A synth.pdf}
}

@article{dacremont_2013,
  title = {Activity in {{Inferior Parietal}} and {{Medial Prefrontal Cortex Signals}} the {{Accumulation}} of {{Evidence}} in a {{Probability Learning Task}}},
  author = {{d'Acremont}, Mathieu and Fornari, Eleonora and Bossaerts, Peter},
  editor = {Behrens, Tim},
  year = {2013},
  month = jan,
  journal = {PLoS Computational Biology},
  volume = {9},
  number = {1},
  pages = {e1002895},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1002895},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JM36UXZU\\d'Acremont et al_2013_PLoS Computational Biology_Activity in Inferior Parietal and Medial Prefrontal Cortex Signals the.pdf}
}

@article{dacremont_2013a,
  title = {The {{Human Brain Encodes Event Frequencies While Forming Subjective Beliefs}}},
  author = {{d'Acremont}, M. and Schultz, W. and Bossaerts, P.},
  year = {2013},
  month = jun,
  journal = {Journal of Neuroscience},
  volume = {33},
  number = {26},
  pages = {10887--10897},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.5829-12.2013},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\G2I9SJ2L\\d'Acremont et al_2013_Journal of Neuroscience_The Human Brain Encodes Event Frequencies While Forming Subjective Beliefs.pdf}
}

@article{dakin_2002,
  title = {What Causes Non-Monotonic Tuning of {{fMRI}} Response to Noisy Images?},
  author = {Dakin, S.C and Hess, R.F and Ledgeway, T and Achtman, R.L},
  year = {2002},
  month = jul,
  journal = {Current Biology},
  volume = {12},
  number = {14},
  pages = {R476-R477},
  issn = {09609822},
  doi = {10.1016/S0960-9822(02)00960-0},
  langid = {english},
  annotation = {00106},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FZ3VMTNE\\Dakin et al. - 2002 - What causes non-monotonic tuning of fMRI response .pdf}
}

@article{dale_,
  title = {Mapping: {{Combining fMRI}} and {{MEG}} for {{High-Resolution Imaging}} of {{Cortical Activity}}},
  author = {Dale, Anders M and Liu, Arthur K and Fischl, Bruce R and Buckner, Randy L and Belliveau, John W and Lewine, Jeffrey D and Halgren, Eric},
  pages = {13},
  abstract = {Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.},
  annotation = {01164},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7YUCXBAH\\Dale et al. - Mapping Combining fMRI and MEG for High-Resolutio.pdf}
}

@article{dale_1997,
  title = {Selective Averaging of Rapidly Presented Individual Trials Using {{fMRI}}},
  author = {Dale, Anders M. and Buckner, Randy L.},
  year = {1997},
  journal = {Human Brain Mapping},
  volume = {5},
  number = {5},
  pages = {329--340},
  issn = {10659471},
  doi = {10.1002/(SICI)1097-0193(1997)5:5<329::AID-HBM1>3.0.CO;2-5},
  abstract = {A major limitation in conducting functional neuroimaging studies, particularly for cognitive experiments, has been the use of blocked task paradigms. Here we explored whether selective averaging techniques similar to those applied in event-related potential (ERP) experiments could be used to demonstrate functional magnetic resonance imaging (fMRI) responses to rapidly intermixed trials. In the first two experiments, we observed that for 1-sec trials of full-field visual checkerboard stimulation, the fMRI blood oxygenation level-dependent (BOLD) signal summated in a roughly linear fashion across successive trials even at very short (2 sec and 5 sec) intertrial intervals, although subtle departures from linearity were observed. In experiments 3 and 4, we observed that it is possible to obtain robust activation maps for rapidly presented randomly mixed trial types (left- and right-hemifield visual checkerboard stimulation) spaced as little as 2 sec apart. Taken collectively, these results suggest that selective averaging may enable fMRI experimental designs identical to those used in typical behavioral and ERP studies. The ability to analyze closely spaced single-trial, or event-related, signals provides for a class of experiments which cannot be conducted using blocked designs. Trial types can be randomly intermixed, and selective averaging based upon trial type and/or subject performance is possible. Hum. Brain Mapping 5:329\textendash 340, 1997.},
  langid = {english},
  annotation = {01070},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FEEKWVGR\\Dale and Buckner - 1997 - Selective averaging of rapidly presented individua.pdf;C\:\\Users\\dirko\\Zotero\\storage\\S5DCR59K\\Dale and Buckner - 1997 - Selective averaging of rapidly presented individua.pdf}
}

@article{dale_1999,
  title = {Optimal Experimental Design for Event-Related {{fMRI}}},
  author = {Dale, Anders M.},
  year = {1999},
  journal = {Human Brain Mapping},
  volume = {8},
  number = {2-3},
  pages = {109--114},
  issn = {1065-9471, 1097-0193},
  doi = {10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W},
  abstract = {An important challenge in the design and analysis of event-related or single-trial functional magnetic resonance imaging (fMRI) experiments is to optimize statistical efficiency, i.e., the accuracy with which the event-related hemodynamic response to different stimuli can be estimated for a given amount of imaging time. Several studies have suggested that using a fixed inter-stimulus-interval (ISI) of at least 15 sec results in optimal statistical efficiency or power and that using shorter ISIs results in a severe loss of power. In contrast, recent studies have demonstrated the feasibility of using ISIs as short as 500 ms while still maintaining considerable efficiency or power. Here, we attempt to resolve this apparent contradiction by a quantitative analysis of the relative efficiency afforded by different event-related experimental designs. This analysis shows that statistical efficiency falls off dramatically as the ISI gets sufficiently short, if the ISI is kept fixed for all trials. However, if the ISI is properly jittered or randomized from trial to trial, the efficiency improves monotonically with decreasing mean ISI. Importantly, the efficiency afforded by such variable ISI designs can be more than 10 times greater than that which can be achieved by fixed ISI designs. These results further demonstrate the feasibility of using identical experimental designs with fMRI and electro-/magnetoencephalography (EEG/MEG) without sacrificing statistical power or efficiency of either technique, thereby facilitating comparison and integration across imaging modalities. Hum. Brain Mapping 8:109\textendash 114, 1999. ௠ 1999 Wiley-Liss, Inc.},
  langid = {english},
  annotation = {01402},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\H48C3WZH\\Dale - 1999 - Optimal experimental design for event-related fMRI.pdf}
}

@article{dale_2001,
  title = {Spatiotemporal Mapping of Brain Activity by Integration of Multiple Imaging Modalities},
  author = {Dale, Anders M and Halgren, Eric},
  year = {2001},
  month = apr,
  journal = {Current Opinion in Neurobiology},
  volume = {11},
  number = {2},
  pages = {202--208},
  issn = {09594388},
  doi = {10.1016/S0959-4388(00)00197-5},
  langid = {english},
  annotation = {00334},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\V7WF5EY4\\Dale and Halgren - 2001 - Spatiotemporal mapping of brain activity by integr.pdf}
}

@article{damasio_1994,
  title = {The Return of {{Phineas Gage}}: Clues about the Brain from the Skull of a Famous Patient},
  shorttitle = {The Return of {{Phineas Gage}}},
  author = {Damasio, H and Grabowski, T and Frank, R and Galaburda, A. and Damasio, A.},
  year = {1994},
  month = may,
  journal = {Science},
  volume = {264},
  number = {5162},
  pages = {1102--1105},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.8178168},
  langid = {english},
  annotation = {01600},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FETJ8LJ5\\Damasio et al. - 1994 - The return of Phineas Gage clues about the brain .pdf}
}

@article{damoiseaux_2009,
  title = {Greater than the Sum of Its Parts: A Review of Studies Combining Structural Connectivity and Resting-State Functional Connectivity},
  shorttitle = {Greater than the Sum of Its Parts},
  author = {Damoiseaux, Jessica S. and Greicius, Michael D.},
  year = {2009},
  month = oct,
  journal = {Brain Structure and Function},
  volume = {213},
  number = {6},
  pages = {525--533},
  issn = {1863-2653, 1863-2661},
  doi = {10.1007/s00429-009-0208-6},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8P68L5IJ\\Damoiseaux_Greicius_2009_Brain Structure and Function_Greater than the sum of its parts.pdf}
}

@article{darlington_,
  title = {Canonical Variate Analysis and Related Techniques},
  author = {Darlington, Richard B and Weinberg, Sharon L},
  journal = {REVIEW OF EDUCATIONAL RESEARCH},
  volume = {43},
  number = {4},
  pages = {22},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WUHP5H5U\\Darlington und Weinberg - Canonical variate analysis and related techniques.pdf}
}

@article{darvas_2004,
  title = {Mapping Human Brain Function with {{MEG}} and {{EEG}}: Methods and Validation},
  shorttitle = {Mapping Human Brain Function with {{MEG}} and {{EEG}}},
  author = {Darvas, F. and Pantazis, D. and {Kucukaltun-Yildirim}, E. and Leahy, R.M.},
  year = {2004},
  month = jan,
  journal = {NeuroImage},
  volume = {23},
  pages = {S289-S299},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2004.07.014},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\IEDFGE43\\Darvas et al_2004_NeuroImage_Mapping human brain function with MEG and EEG.pdf}
}

@book{darwiche_2009,
  title = {Modeling and Reasoning with {{Bayesian}} Networks},
  author = {Darwiche, Adnan},
  year = {2009},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge ; New York}},
  isbn = {978-0-521-88438-9},
  langid = {english},
  lccn = {QA279.5 .D37 2009},
  keywords = {Bayesian statistical decision theory,Graphic methods,Inference,Modeling,Probabilities},
  annotation = {01163  OCLC: ocn263146943},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4FZZQQ8Z\\Darwiche - 2009 - Modeling and reasoning with Bayesian networks.pdf}
}

@article{daunizeau_,
  title = {Semi-Analytical Approximations to Statistical Moments of Sigmoid and Softmax Mappings of Normal Variables},
  author = {Daunizeau, J},
  pages = {29},
  annotation = {00000},
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}

@article{daunizeau_2006,
  title = {Bayesian {{Spatio-Temporal Approach}} for {{EEG Source Reconstruction}}: {{Conciliating ECD}} and {{Distributed Models}}},
  shorttitle = {Bayesian {{Spatio-Temporal Approach}} for {{EEG Source Reconstruction}}},
  author = {Daunizeau, J. and Mattout, J. and Clonda, D. and Goulard, B. and Benali, H. and Lina, J.-M.},
  year = {2006},
  month = mar,
  journal = {IEEE Transactions on Biomedical Engineering},
  volume = {53},
  number = {3},
  pages = {503--516},
  issn = {0018-9294},
  doi = {10.1109/TBME.2005.869791},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9LU2W4FI\\Daunizeau et al_2006_IEEE Transactions on Biomedical Engineering_Bayesian Spatio-Temporal Approach for EEG Source Reconstruction.pdf}
}

@article{daunizeau_2007,
  title = {A Mesostate-Space Model for {{EEG}} and {{MEG}}},
  author = {Daunizeau, Jean and Friston, Karl J.},
  year = {2007},
  month = oct,
  journal = {NeuroImage},
  volume = {38},
  number = {1},
  pages = {67--81},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.06.034},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RCDRMZZ8\\Daunizeau_Friston_2007_NeuroImage_A mesostate-space model for EEG and MEG.pdf;C\:\\Users\\dirko\\Zotero\\storage\\WRERALY7\\Daunizeau_Friston_2007_NeuroImage_A mesostate-space model for EEG and MEG.pdf}
}

@article{daunizeau_2007a,
  title = {Symmetrical Event-Related {{EEG}}/{{fMRI}} Information Fusion in a Variational {{Bayesian}} Framework},
  author = {Daunizeau, Jean and Grova, Christophe and Marrelec, Guillaume and Mattout, J{\'e}r{\'e}mie and Jbabdi, Saad and {P{\'e}l{\'e}grini-Issac}, M{\'e}lanie and Lina, Jean-Marc and Benali, Habib},
  year = {2007},
  month = may,
  journal = {NeuroImage},
  volume = {36},
  number = {1},
  pages = {69--87},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.01.044},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AIMCJL2Z\\Daunizeau et al_2007_NeuroImage_Symmetrical event-related EEG-fMRI information fusion in a variational Bayesian.pdf;C\:\\Users\\dirko\\Zotero\\storage\\KJYDE4EK\\Daunizeau et al_2007_NeuroImage_Symmetrical event-related EEG-fMRI information fusion in a variational Bayesian.pdf}
}

@article{daunizeau_2009,
  title = {Dynamic Causal Modelling of Distributed Electromagnetic Responses},
  author = {Daunizeau, Jean and Kiebel, Stefan J. and Friston, Karl J.},
  year = {2009},
  month = aug,
  journal = {NeuroImage},
  volume = {47},
  number = {2},
  pages = {590--601},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.04.062},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\U4GUFJ97\\Daunizeau et al_2009_NeuroImage_Dynamic causal modelling of distributed electromagnetic responses.pdf}
}

@incollection{daunizeau_2009a,
  title = {{{EEG}}\textendash{{fMRI}} Information Fusion: Biophysics and Data Analysis},
  shorttitle = {{{EEG}}\textendash{{fMRI}} Information Fusion},
  booktitle = {{{EEG-fMRI}}},
  author = {Daunizeau, Jean and Laufs, Helmut and Friston, Karl J.},
  year = {2009},
  pages = {511--526},
  publisher = {{Springer}},
  annotation = {00031},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\S495R3TE\\Daunizeau_Laufs_Friston_Fusion_Book_Chapter.pdf}
}

@article{daunizeau_2009b,
  title = {Variational {{Bayesian}} Identification and Prediction of Stochastic Nonlinear Dynamic Causal Models},
  author = {Daunizeau, J. and Friston, K.J. and Kiebel, S.J.},
  year = {2009},
  month = nov,
  journal = {Physica D: Nonlinear Phenomena},
  volume = {238},
  number = {21},
  pages = {2089--2118},
  issn = {01672789},
  doi = {10.1016/j.physd.2009.08.002},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5NSHN5EP\\Daunizeau et al. - 2009 - Variational Bayesian identification and prediction.pdf;C\:\\Users\\dirko\\Zotero\\storage\\CG9NFKVQ\\Daunizeau et al_2009_Physica D Nonlinear Phenomena_Variational Bayesian identification and prediction of stochastic nonlinear.pdf}
}

@article{daunizeau_2010,
  title = {Observing the {{Observer}} ({{I}}): {{Meta-Bayesian Models}} of {{Learning}} and {{Decision-Making}}},
  shorttitle = {Observing the {{Observer}} ({{I}})},
  author = {Daunizeau, Jean and {den Ouden}, Hanneke E. M. and Pessiglione, Matthias and Kiebel, Stefan J. and Stephan, Klaas E. and Friston, Karl J.},
  editor = {Sporns, Olaf},
  year = {2010},
  month = dec,
  journal = {PLoS ONE},
  volume = {5},
  number = {12},
  pages = {e15554},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0015554},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QH5MQ2XV\\Daunizeau et al_2010_PLoS ONE_Observing the Observer (I).pdf}
}

@article{daunizeau_2010a,
  title = {Observing the {{Observer}} ({{II}}): {{Deciding When}} to {{Decide}}},
  shorttitle = {Observing the {{Observer}} ({{II}})},
  author = {Daunizeau, Jean and {den Ouden}, Hanneke E. M. and Pessiglione, Matthias and Kiebel, Stefan J. and Friston, Karl J. and Stephan, Klaas E.},
  editor = {Sporns, Olaf},
  year = {2010},
  month = dec,
  journal = {PLoS ONE},
  volume = {5},
  number = {12},
  pages = {e15555},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0015555},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NTUP5LSL\\Daunizeau et al_2010_PLoS ONE_Observing the Observer (II).pdf}
}

@article{daunizeau_2011,
  title = {Dynamic Causal Modelling: {{A}} Critical Review of the Biophysical and Statistical Foundations},
  shorttitle = {Dynamic Causal Modelling},
  author = {Daunizeau, J. and David, O. and Stephan, K.E.},
  year = {2011},
  month = sep,
  journal = {NeuroImage},
  volume = {58},
  number = {2},
  pages = {312--322},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.11.062},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JJFIDXKC\\Daunizeau et al_2011_NeuroImage_Dynamic causal modelling.pdf}
}

@article{daunizeau_2011a,
  title = {Optimizing {{Experimental Design}} for {{Comparing Models}} of {{Brain Function}}},
  author = {Daunizeau, Jean and Preuschoff, Kerstin and Friston, Karl and Stephan, Klaas},
  editor = {Sporns, Olaf},
  year = {2011},
  month = nov,
  journal = {PLoS Computational Biology},
  volume = {7},
  number = {11},
  pages = {e1002280},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1002280},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\I6D85WPE\\Daunizeau et al_2011_PLoS Computational Biology_Optimizing Experimental Design for Comparing Models of Brain Function.pdf;C\:\\Users\\dirko\\Zotero\\storage\\XX5GFTY3\\Daunizeau et al_2011_PLoS Computational Biology_Optimizing Experimental Design for Comparing Models of Brain Function.pdf}
}

@article{daunizeau_2012,
  title = {Stochastic Dynamic Causal Modelling of {{fMRI}} Data: {{Should}} We Care about Neural Noise?},
  shorttitle = {Stochastic Dynamic Causal Modelling of {{fMRI}} Data},
  author = {Daunizeau, J. and Stephan, K.E. and Friston, K.J.},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {1},
  pages = {464--481},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.04.061},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KZAILMD8\\Daunizeau et al_2012_NeuroImage_Stochastic dynamic causal modelling of fMRI data.pdf}
}

@article{daunizeau_2013,
  title = {An Electrophysiological Validation of Stochastic {{DCM}} for {{fMRI}}},
  author = {Daunizeau, J. and Lemieux, L. and Vaudano, A. E. and Friston, K. J. and Stephan, K. E.},
  year = {2013},
  journal = {Frontiers in Computational Neuroscience},
  volume = {6},
  issn = {1662-5188},
  doi = {10.3389/fncom.2012.00103},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PCNM5H6S\\Daunizeau et al_2013_Frontiers in Computational Neuroscience_An electrophysiological validation of stochastic DCM for fMRI.pdf}
}

@article{daunizeau_a,
  title = {The Variational {{Laplace}} Approach to Approximate {{Bayesian}} Inference},
  author = {Daunizeau, J},
  pages = {20},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2H23AHZG\\Daunizeau - The variational Laplace approach to approximate Ba.pdf;C\:\\Users\\dirko\\Zotero\\storage\\HGJ3KH6J\\Daunizeau - The variational Laplace approach to approximate Ba.pdf}
}

@techreport{davenport_2021,
  type = {Preprint},
  title = {The Expected Behaviour of Random Fields in High Dimensions: Contradictions in the Results of {{Bansal}} and {{Peterson}} (2018)},
  shorttitle = {The Expected Behaviour of Random Fields in High Dimensions},
  author = {Davenport, Samuel and Nichols, Thomas E.},
  year = {2021},
  month = jan,
  institution = {{Neuroscience}},
  doi = {10.1101/2021.01.21.427611},
  abstract = {Bansal and Peterson (2018) found that in simple stationary Gaussian simulations Random Field Theory incorrectly estimates the number of clusters of a Gaussian field that lie above a threshold. Their results contradict the existing literature and appear to have arisen due to errors in their code. Using reproducible code we demonstrate that in their simulations Random Field Theory correctly predicts the expected number of clusters and therefore that many of their results are invalid.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JBE7TB69\\Davenport und Nichols - 2021 - The expected behaviour of random fields in high di.pdf}
}

@book{david_2001,
  title = {Annotated {{Readings}} in the {{History}} of {{Statistics}}},
  author = {David, H. A. and Edwards, A. W. F.},
  year = {2001},
  series = {Springer {{Series}} in {{Statistics}}},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4757-3500-0},
  isbn = {978-1-4419-3174-0 978-1-4757-3500-0},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2BSCXPK4\\David_Edwards_2001_Annotated Readings in the History of Statistics.pdf}
}

@article{david_2003,
  title = {A Neural Mass Model for {{MEG}}/{{EEG}}:},
  shorttitle = {A Neural Mass Model for {{MEG}}/{{EEG}}},
  author = {David, Olivier and Friston, Karl J.},
  year = {2003},
  month = nov,
  journal = {NeuroImage},
  volume = {20},
  number = {3},
  pages = {1743--1755},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2003.07.015},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LGI3JV7L\\David_Friston_2003_NeuroImage_A neural mass model for MEG-EEG.pdf;C\:\\Users\\dirko\\Zotero\\storage\\MLEEP77U\\David_NI_03.pdf;C\:\\Users\\dirko\\Zotero\\storage\\ZMVD8GWB\\David and Friston - 2003 - A neural mass model for MEGEEG.pdf}
}

@article{david_2005,
  title = {Modelling Event-Related Responses in the Brain},
  author = {David, Olivier and Harrison, Lee and Friston, Karl J.},
  year = {2005},
  month = apr,
  journal = {NeuroImage},
  volume = {25},
  number = {3},
  pages = {756--770},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2004.12.030},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CBKV5WQC\\David et al_2005_NeuroImage_Modelling event-related responses in the brain.pdf;C\:\\Users\\dirko\\Zotero\\storage\\E44KY9W3\\David et al. - 2005 - Modelling event-related responses in the brain.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PTTM96FY\\David_NI_05.pdf}
}

@article{david_2006,
  title = {Dynamic Causal Modeling of Evoked Responses in {{EEG}} and {{MEG}}},
  author = {David, Olivier and Kiebel, Stefan J. and Harrison, Lee M. and Mattout, J{\'e}r{\'e}mie and Kilner, James M. and Friston, Karl J.},
  year = {2006},
  month = may,
  journal = {NeuroImage},
  volume = {30},
  number = {4},
  pages = {1255--1272},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2005.10.045},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7KXQLUCU\\David et al_2006_NeuroImage_Dynamic causal modeling of evoked responses in EEG and MEG.pdf}
}

@article{david_2008,
  title = {Identifying {{Neural Drivers}} with {{Functional MRI}}: {{An Electrophysiological Validation}}},
  shorttitle = {Identifying {{Neural Drivers}} with {{Functional MRI}}},
  author = {David, Olivier and Guillemain, Isabelle and Saillet, Sandrine and Reyt, Sebastien and Deransart, Colin and Segebarth, Christoph and Depaulis, Antoine},
  editor = {{Valdes-Sosa}, Pedro},
  year = {2008},
  month = dec,
  journal = {PLoS Biology},
  volume = {6},
  number = {12},
  pages = {e315},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.0060315},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YZSBZL5U\\David et al_2008_PLoS Biology_Identifying Neural Drivers with Functional MRI.pdf}
}

@article{david_2011,
  title = {{{fMRI}} Connectivity, Meaning and Empiricism},
  author = {David, Olivier},
  year = {2011},
  month = sep,
  journal = {NeuroImage},
  volume = {58},
  number = {2},
  pages = {306--309},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.09.073},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8XP9ZH8G\\David_2011_NeuroImage_fMRI connectivity, meaning and empiricism.pdf}
}

@article{davidharville_1976,
  title = {Extension of the {{Gauss-Markov Theorem}} to {{Include}} the {{Estimation}} of {{Random Effects}}},
  author = {David Harville},
  year = {1976},
  journal = {The Annals of Statistics},
  volume = {4},
  number = {2},
  pages = {384--395},
  annotation = {00000},
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}

@article{daw_2005,
  title = {Uncertainty-Based Competition between Prefrontal and Dorsolateral Striatal Systems for Behavioral Control},
  author = {Daw, Nathaniel D. and Niv, Yael and Dayan, Peter},
  year = {2005},
  journal = {Nature Neuroscience},
  volume = {8},
  number = {12},
  pages = {1704--1711},
  issn = {10976256},
  doi = {10.1038/nn1560},
  abstract = {A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.},
  pmid = {16286932},
  annotation = {00000}
}

@article{daw_2006,
  ids = {daw2006},
  title = {Cortical Substrates for Exploratory Decisions in Humans},
  author = {Daw, Nathaniel D. and O'Doherty, John P. and Dayan, Peter and Seymour, Ben and Dolan, Raymond J.},
  year = {2006},
  journal = {Nature},
  volume = {441},
  number = {7095},
  pages = {876--879},
  publisher = {{Nature Publishing Group}},
  issn = {14764687},
  doi = {10.1038/nature04766},
  abstract = {Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.},
  pmid = {16778890},
  annotation = {00000}
}

@article{daw_2006a,
  title = {Representation and Timing in Theories of the Dopamine System},
  author = {Daw, Nathaniel D. and Courville, Aaron C. and Touretzky, David S.},
  year = {2006},
  journal = {Neural computation},
  volume = {18},
  number = {7},
  pages = {1637--1677},
  annotation = {00118},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\R4QVSXTY\\Daw_NC_06.pdf}
}

@article{daw_2011,
  title = {Model-Based Influences on Humans' Choices and Striatal Prediction Errors},
  author = {Daw, Nathaniel D. and Gershman, Samuel J. and Seymour, Ben and Dayan, Peter and Dolan, Raymond J.},
  year = {2011},
  journal = {Neuron},
  volume = {69},
  number = {6},
  pages = {1204--1215},
  issn = {08966273},
  doi = {10.1016/j.neuron.2011.02.027},
  abstract = {The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making. \textcopyright{} 2011 Elsevier Inc.},
  pmid = {21435563},
  annotation = {00000}
}

@article{dayan_1992,
  title = {Q-Learning},
  author = {Dayan, Peter and Watkins, CJCH},
  year = {1992},
  journal = {Machine learning},
  volume = {8},
  number = {3},
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}

@book{dayan_2001,
  title = {Theoretical Neuroscience},
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  year = {2001},
  volume = {806},
  publisher = {{Cambridge, MA: MIT Press}},
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}

@article{dayan_2008,
  ids = {dayan2008,dayan_decision_2008-1},
  title = {Decision Theory, Reinforcement Learning, and the Brain},
  author = {Dayan, P. and Daw, N. D.},
  year = {2008},
  month = dec,
  journal = {Cognitive, Affective, \& Behavioral Neuroscience},
  volume = {8},
  number = {4},
  pages = {429--453},
  publisher = {{Springer}},
  issn = {1530-7026, 1531-135X},
  doi = {10.3758/CABN.8.4.429},
  langid = {english},
  pmid = {19033240},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HWQ2C4KC\\Dayan und Daw - 2008 - Decision theory, reinforcement learning, and the b.pdf}
}

@article{dayan_2012,
  title = {How to Set the Switches on This Thing},
  author = {Dayan, Peter},
  year = {2012},
  journal = {Current Opinion in Neurobiology},
  volume = {22},
  number = {6},
  pages = {1068--1074},
  issn = {18736882},
  doi = {10.1016/j.conb.2012.05.011},
  abstract = {Reinforcement learning (RL) has become a dominant computational paradigm for modeling psychological and neural aspects of affectively charged decision-making tasks. RL is normally construed in terms of the interaction between a subject and its environment, with the former emitting actions, and the latter providing stimuli, and appetitive and aversive reinforcement. However, there is recent emphasis on redrawing the boundary between the two, with the organism constructing its own notion of reward, punishment and state, and with internal actions, such as the gating of working memory, being treated on an equal footing with external manipulation of the environment. We review recent work in this area, focusing on cognitive control.},
  pmid = {22704797},
  annotation = {00000}
}

@article{dayan_2014,
  ids = {dayan_rationalizable_2014-1},
  title = {Rationalizable {{Irrationalities}} of {{Choice}}},
  author = {Dayan, Peter},
  year = {2014},
  month = apr,
  journal = {Topics in Cognitive Science},
  volume = {6},
  number = {2},
  pages = {204--228},
  issn = {17568757},
  doi = {10.1111/tops.12082},
  langid = {english},
  pmid = {24648392},
  keywords = {Bounded rationality,Model-based,Model-free,Noisy decision-making,Pavlovian,Pruning,Reinforcement learning},
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}

@article{dearaujo_2003,
  title = {Shannon Entropy Applied to the Analysis of Event-Related {{fMRI}} Time Series},
  author = {{de Araujo}, D.B and Tedeschi, W and Santos, A.C and Elias, J and Neves, U.P.C and Baffa, O},
  year = {2003},
  month = sep,
  journal = {NeuroImage},
  volume = {20},
  number = {1},
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  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00306-9},
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}

@book{debauchies_1998,
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  author = {Debauchies, Ingrid},
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}

@article{debener_,
  title = {Using {{ICA}} for the {{Analysis}} of {{Multi-Channel EEG Data}}},
  author = {Debener, Stefan and Thorne, Jeremy and Schneider, Till R and Viola, Filipa Campos},
  pages = {13},
  langid = {english},
  annotation = {00063},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XR8QEVCN\\Debener et al. - Using ICA for the Analysis of Multi-Channel EEG Da.pdf}
}

@article{debener_2005,
  title = {Trial-by-{{Trial Coupling}} of {{Concurrent Electroencephalogram}} and {{Functional Magnetic Resonance Imaging Identifies}} the {{Dynamics}} of {{Performance Monitoring}}},
  author = {Debener, S.},
  year = {2005},
  month = dec,
  journal = {Journal of Neuroscience},
  volume = {25},
  number = {50},
  pages = {11730--11737},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3286-05.2005},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9DEU5DE6\\Debener_2005_Journal of Neuroscience_Trial-by-Trial Coupling of Concurrent Electroencephalogram and Functional.pdf}
}

@article{debener_2006,
  title = {Single-Trial {{EEG}}\textendash{{fMRI}} Reveals the Dynamics of Cognitive Function},
  author = {Debener, Stefan and Ullsperger, Markus and Siegel, Markus and Engel, Andreas K.},
  year = {2006},
  month = dec,
  journal = {Trends in Cognitive Sciences},
  volume = {10},
  number = {12},
  pages = {558--563},
  issn = {13646613},
  doi = {10.1016/j.tics.2006.09.010},
  langid = {english},
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}

@article{deco_2008,
  title = {The {{Dynamic Brain}}: {{From Spiking Neurons}} to {{Neural Masses}} and {{Cortical Fields}}},
  shorttitle = {The {{Dynamic Brain}}},
  author = {Deco, Gustavo and Jirsa, Viktor K. and Robinson, Peter A. and Breakspear, Michael and Friston, Karl},
  editor = {Sporns, Olaf},
  year = {2008},
  month = aug,
  journal = {PLoS Computational Biology},
  volume = {4},
  number = {8},
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  doi = {10.1371/journal.pcbi.1000092},
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}

@article{deco_2013,
  title = {Brain Mechanisms for Perceptual and Reward-Related Decision-Making},
  author = {Deco, Gustavo and Rolls, Edmund T. and Albantakis, Larissa and Romo, Ranulfo},
  year = {2013},
  month = apr,
  journal = {Progress in Neurobiology},
  volume = {103},
  pages = {194--213},
  issn = {03010082},
  doi = {10.1016/j.pneurobio.2012.01.010},
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}

@article{deco_2018,
  title = {Whole-{{Brain Multimodal Neuroimaging Model Using Serotonin Receptor Maps Explains Non-linear Functional Effects}} of {{LSD}}},
  author = {Deco, Gustavo and Cruzat, Josephine and Cabral, Joana and Knudsen, Gitte M. and {Carhart-Harris}, Robin L. and Whybrow, Peter C. and Logothetis, Nikos K. and Kringelbach, Morten L.},
  year = {2018},
  month = oct,
  journal = {Current Biology},
  volume = {28},
  number = {19},
  pages = {3065-3074.e6},
  issn = {09609822},
  doi = {10.1016/j.cub.2018.07.083},
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}

@article{decockbruning_2011,
  title = {The Legal Status of Research Data in the {{Knowledge Exchange}} Partner Countries},
  author = {De Cock Bruning, M. and {van Dither}, B. and {Jeppersen de Boer}, C. G. and Ringnalda, A.},
  year = {2011},
  journal = {Available on: http://www. knowledge-exchange. info/Default. aspx},
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}

@article{defreitas_2001,
  title = {Christophe {{Andrieu}}},
  author = {{de Freitas}, Nando and Doucet, Arnaud and Jordan, Michael I.},
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}

@article{degras_2014,
  title = {A Hierarchical Model for Simultaneous Detection and Estimation in Multi-Subject {{fMRI}} Studies},
  author = {Degras, David and Lindquist, Martin A.},
  year = {2014},
  month = sep,
  journal = {NeuroImage},
  volume = {98},
  pages = {61--72},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2014.04.052},
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}

@book{degroot_2012,
  ids = {degroot_probability_2012a},
  title = {Probability and Statistics},
  author = {DeGroot, Morris H. and Schervish, Mark J.},
  year = {2012},
  edition = {4th ed},
  publisher = {{Addison-Wesley}},
  address = {{Boston}},
  isbn = {978-0-321-50046-5},
  langid = {english},
  lccn = {QA273 .D35 2012},
  keywords = {Mathematical statistics,Probabilities,Textbooks},
  annotation = {04021  OCLC: ocn502674206},
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}

@book{deisenroth_2020,
  title = {Mathematics for {{Machine Learning}}},
  author = {Deisenroth, Marc Peter and Faisal, A. Aldo and Ong, Cheng Soon},
  year = {2020},
  month = feb,
  edition = {First},
  publisher = {{Cambridge University Press}},
  doi = {10.1017/9781108679930},
  isbn = {978-1-108-67993-0 978-1-108-47004-9 978-1-108-45514-5},
  langid = {english},
  annotation = {00039},
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}

@article{delaney-busch_2019,
  title = {Neural Evidence for {{Bayesian}} Trial-by-Trial Adaptation on the {{N400}} during Semantic Priming},
  author = {{Delaney-Busch}, Nathaniel and Morgan, Emily and Lau, Ellen and Kuperberg, Gina R.},
  year = {2019},
  month = jun,
  journal = {Cognition},
  volume = {187},
  pages = {10--20},
  issn = {00100277},
  doi = {10.1016/j.cognition.2019.01.001},
  abstract = {When semantic information is activated by a context prior to new bottom-up input (i.e. when a word is predicted), semantic processing of that incoming word is typically facilitated, attenuating the amplitude of the N400 event related potential (ERP) \textendash{} a direct neural measure of semantic processing. N400 modulation is observed even when the context is a single semantically related ``prime'' word. This so-called ``N400 semantic priming effect'' is sensitive to the probability of encountering a related prime-target pair within an experimental block, suggesting that participants may be adapting the strength of their predictions to the predictive validity of their broader experimental environment. We formalize this adaptation using a Bayesian learning model that estimates and updates the probability of encountering a related versus an unrelated prime-target pair on each successive trial. We found that our model's trial-by-trial estimates of target word probability accounted for significant variance in trial-by-trial N400 amplitude. These findings suggest that Bayesian principles contribute to how comprehenders adapt their semantic predictions to the statistical structure of their broader environment, with implications for the functional significance of the N400 component and the predictive nature of language processing.},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\E7Q7IG7S\\Delaney-Busch et al. - 2019 - Neural evidence for Bayesian trial-by-trial adapta.pdf}
}

@article{delorme_2012,
  title = {Independent {{EEG Sources Are Dipolar}}},
  author = {Delorme, Arnaud and Palmer, Jason and Onton, Julie and Oostenveld, Robert and Makeig, Scott},
  editor = {Ward, Lawrence M.},
  year = {2012},
  month = feb,
  journal = {PLoS ONE},
  volume = {7},
  number = {2},
  pages = {e30135},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0030135},
  abstract = {Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition `dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonlyused decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partiallysynchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).},
  langid = {english},
  annotation = {00310},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GHKFEZSP\\Delorme et al. - 2012 - Independent EEG Sources Are Dipolar.PDF}
}

@article{demartino_2010,
  title = {Multimodal Imaging: An Evaluation of Univariate and Multivariate Methods for Simultaneous {{EEG}}/{{fMRI}}},
  shorttitle = {Multimodal Imaging},
  author = {De Martino, Federico and Valente, Giancarlo and {de Borst}, Aline W. and Esposito, Fabrizio and Roebroeck, Alard and Goebel, Rainer and Formisano, Elia},
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  month = oct,
  journal = {Magnetic Resonance Imaging},
  volume = {28},
  number = {8},
  pages = {1104--1112},
  issn = {0730725X},
  doi = {10.1016/j.mri.2009.12.026},
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}

@article{dempster_1977,
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  doi = {10.1111/j.2517-6161.1977.tb01600.x},
  abstract = {A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.},
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}

@article{dempster_1981,
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}

@article{demunck_2007,
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}

@article{demunck_2009,
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  journal = {NeuroImage},
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}

@article{deneux_2010,
  title = {{{EEG-fMRI Fusion}} of {{Paradigm-Free Activity Using Kalman Filtering}}},
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  doi = {10.1162/neco.2009.05-08-793},
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}

@article{deneve_2012,
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@article{dennisv.lindley_,
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}

@article{dennisv.lindley_a,
  title = {The {{Choice}} of {{Sample Size-A Reply}} to the {{Discussion}}},
  author = {Dennis V. Lindley},
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@article{denouden_2010,
  title = {Striatal {{Prediction Error Modulates Cortical Coupling}}},
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}

@article{derrington_1984,
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  abstract = {1. The discharges of single neurones in the parvocellular and magnocellular laminae of the macaque's lateral geniculate nucleus (l.g.n.) were recorded with glass-insulated tungsten micro-electrodes.},
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@article{dersimonian_1986,
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@article{desimone_1995,
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@article{desmond_2002,
  title = {Estimating Sample Size in Functional {{MRI}} ({{fMRI}}) Neuroimaging Studies: {{Statistical}} Power Analyses},
  shorttitle = {Estimating Sample Size in Functional {{MRI}} ({{fMRI}}) Neuroimaging Studies},
  author = {Desmond, John E and Glover, Gary H},
  year = {2002},
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  journal = {Journal of Neuroscience Methods},
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  abstract = {Estimation of statistical power in functional MRI (fMRI) requires knowledge of the expected percent signal change between two conditions as well as estimates of the variability in percent signal change. Variability can be divided into intra-subject variability, reflecting noise within the time series, and inter-subject variability, reflecting subject-to-subject differences in activation. The purpose of this study was to obtain estimates of percent signal change and the two sources of variability from fMRI data, and then use these parameter estimates in simulation experiments in order to generate power curves. Of interest from these simulations were conclusions concerning how many subjects are needed and how many time points within a scan are optimal in an fMRI study of cognitive function. Intra-subject variability was estimated from resting conditions, and inter-subject variability and percent signal change were estimated from verbal working memory data. Simulations derived from these parameters illustrate how percent signal change, intra- and inter-subject variability, and number of time points affect power. An empirical test experiment, using fMRI data acquired during somatosensory stimulation, showed good correspondence between the simulation-based power predictions and the power observed within somatosensory regions of interest. Our analyses suggested that for a liberal threshold of 0.05, about 12 subjects were required to achieve 80\% power at the single voxel level for typical activations. At more realistic thresholds, that approach those used after correcting for multiple comparisons, the number of subjects doubled to maintain this level of power. \# 2002 Elsevier Science B.V. All rights reserved.},
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@incollection{deuflhard_1996,
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  booktitle = {Scientific {{Computing}} in {{Chemical Engineering}}},
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  address = {{Berlin, Heidelberg}},
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  abstract = {The paper analyzes a splitting technique into fast and slow dynamical components of ODE systems as suggested by Maas and Pope recently. Their technique is based on a real block \textendash{} Schur decomposition of the Jacobian of the right hand side of the ODE. As a result of the analysis, a computationally cheap monitor for the possible necessary recovering of the splitting is derived by singular perturbation theory. Numerical experiments on moderate size, but challenging reaction kinetics problems document the efficiency of the new device within a linearly-implicit stiff integrator.},
  isbn = {978-3-642-80151-8 978-3-642-80149-5},
  langid = {english},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\WMPTCKXA\\Deuflhard and Heroth - 1996 - Dynamic Dimension Reduction in ODE Models.pdf}
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@misc{deutscherbundestag_2016,
  title = {Auslegungsgrunds\"atze Des {{EuGH}}},
  author = {Deutscher Bundestag},
  year = {2016},
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}

@article{devor_2003,
  title = {Coupling of {{Total Hemoglobin Concentration}}, {{Oxygenation}}, and {{Neural Activity}} in {{Rat Somatosensory Cortex}}},
  author = {Devor, Anna and Dunn, Andrew K. and Andermann, Mark L. and Ulbert, Istvan and Boas, David A. and Dale, Anders M.},
  year = {2003},
  month = jul,
  journal = {Neuron},
  volume = {39},
  number = {2},
  pages = {353--359},
  issn = {08966273},
  doi = {10.1016/S0896-6273(03)00403-3},
  abstract = {Recent advances in brain imaging techniques, including functional magnetic resonance imaging (fMRI), offer great promise for noninvasive mapping of brain function. However, the indirect nature of the imaging signals to the underlying neural activity limits the interpretation of the resulting maps. The present report represents the first systematic study with sufficient statistical power to quantitatively characterize the relationship between changes in blood oxygen content and the neural spiking and synaptic activity.},
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@book{dickhaus_2013,
  title = {Simultaneous Statistical Inference},
  author = {Dickhaus, Thorsten},
  year = {2013},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-3-642-45181-2},
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}

@inproceedings{dietrich_2014,
  title = {Ethics in {{Data Sharing}}: {{Developing}} a {{Model}} for {{Best Practice}}},
  shorttitle = {Ethics in {{Data Sharing}}},
  author = {Dietrich, Sven and Van Der Ham, Jeroen and Pras, Aiko and Van Rijswijk Deij, Roland and Shou, Darren and Sperotto, Anna and Van Wynsberghe, Aimee and Zuck, Lenore D.},
  year = {2014},
  month = may,
  pages = {5--9},
  publisher = {{IEEE}},
  doi = {10.1109/SPW.2014.43},
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@article{dimke_2019,
  title = {Most Scientists Prefer Small and Mid-Sized Research Grants},
  author = {Dimke, Henrik and Norn, Maria Theresa and Christiansen, Peter Munk and Wohlert, Jeppe and Zinner, Nikolaj Thomas},
  year = {2019},
  month = jun,
  journal = {Nature Human Behaviour},
  issn = {2397-3374},
  doi = {10.1038/s41562-019-0643-1},
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@article{dinhtuanpham_1997,
  title = {Blind Separation of Mixture of Independent Sources through a Quasi-Maximum Likelihood Approach},
  author = {{Dinh Tuan Pham} and Garat, P.},
  year = {1997},
  month = jul,
  journal = {IEEE Transactions on Signal Processing},
  volume = {45},
  number = {7},
  pages = {1712--1725},
  issn = {1053587X},
  doi = {10.1109/78.599941},
  abstract = {In this paper, we propose two methods for separating mixtures of independent sources without any precise knowledge of their probability distribution. They are obtained by considering a maximum likelihood (ML) solution corresponding to some given distributions of the sources and relaxing this assumption afterward. The first method is specially adapted to temporally independent non-Gaussian sources and is based on the use of nonlinear separating functions. The second method is specially adapted to correlated sources with distinct spectra and is based on the use of linear separating filters. A theoretical analysis of the performance of the methods has been made. A simple procedure for optimally choosing the separating functions from a given linear space of functions is proposed. Further, in the second method, a simple implementation based on the simultaneous diagonalization of two symmetric matrices is provided. Finally, some numerical and simulation results are given, illustrating the performance of the method and the good agreement between the experiments and the theory.},
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@article{dirusso_2002,
  title = {Cortical Sources of the Early Components of the Visual Evoked Potential: {{Cortical Sources}} of {{VEP}}},
  shorttitle = {Cortical Sources of the Early Components of the Visual Evoked Potential},
  author = {Di Russo, Francesco and Mart{\'i}nez, Ant{\'i}gona and Sereno, Martin I. and Pitzalis, Sabrina and Hillyard, Steven A.},
  year = {2002},
  month = feb,
  journal = {Human Brain Mapping},
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  number = {2},
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}

@article{dirusso_2003,
  title = {Source {{Analysis}} of {{Event-related Cortical Activity}} during {{Visuo-spatial Attention}}},
  author = {Di Russo, F.},
  year = {2003},
  month = may,
  journal = {Cerebral Cortex},
  volume = {13},
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}

@article{dirusso_2007,
  title = {Spatiotemporal Analysis of the Cortical Sources of the Steady-State Visual Evoked Potential},
  author = {Di Russo, Francesco and Pitzalis, Sabrina and Aprile, Teresa and Spitoni, Grazia and Patria, Fabiana and Stella, Alessandra and Spinelli, Donatella and Hillyard, Steven A.},
  year = {2007},
  month = apr,
  journal = {Human Brain Mapping},
  volume = {28},
  number = {4},
  pages = {323--334},
  issn = {10659471, 10970193},
  doi = {10.1002/hbm.20276},
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}

@article{djuric_2000,
  title = {Estimation of a {{Bernoulli}} Parameter p from Imperfect Trials},
  author = {Djuric, P.M. and Huang, Y.},
  year = {2000},
  month = jun,
  journal = {IEEE Signal Processing Letters},
  volume = {7},
  number = {6},
  pages = {160--163},
  issn = {1070-9908, 1558-2361},
  doi = {10.1109/97.844638},
  abstract = {Imperfect Bernoulli trials arise when the outcome of a Bernoulli experiment is not known with certainty. In signal processing, we often need to estimate a probability of occurrence of an event from imperfect Bernoulli trials. A typical example is the estimation of the probability of a signal being present in noisy data. In his famous essay, Bayes solved the same problem but for perfect trials. In this letter, a solution is provided for imperfect trials. It is shown that it includes Bayes' solution as a special case.},
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}

@book{dobson_2002,
  title = {An Introduction to Generalized Linear Models},
  author = {Dobson, Annette J.},
  year = {2002},
  series = {Chapman \& {{Hall}}/{{CRC}} Texts in Statistical Science Series},
  edition = {2nd ed},
  publisher = {{Chapman \& Hall/CRC}},
  address = {{Boca Raton}},
  isbn = {978-1-58488-165-0},
  lccn = {QA276 .D589 2002},
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@article{doeller_2003,
  title = {Prefrontal Cortex Involvement in Preattentive Auditory Deviance Detection:},
  shorttitle = {Prefrontal Cortex Involvement in Preattentive Auditory Deviance Detection},
  author = {Doeller, Christian F and Opitz, Bertram and Mecklinger, Axel and Krick, Christoph and Reith, Wolfgang and Schr{\"o}ger, Erich},
  year = {2003},
  month = oct,
  journal = {NeuroImage},
  volume = {20},
  number = {2},
  pages = {1270--1282},
  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00389-6},
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}

@article{doersch_2016,
  title = {Tutorial on {{Variational Autoencoders}}},
  author = {Doersch, Carl},
  year = {2016},
  month = jun,
  journal = {arXiv:1606.05908 [cs, stat]},
  eprint = {1606.05908},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits [1, 2], faces [1, 3, 4], house numbers [5, 6], CIFAR images [6], physical models of scenes [4], segmentation [7], and predicting the future from static images [8]. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {00245},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YPSGQVHW\\Doersch - 2016 - Tutorial on Variational Autoencoders.pdf}
}

@article{dohmatob_2018,
  title = {Inter-Subject {{Registration}} of {{Functional Images}}: {{Do We Need Anatomical Images}}?},
  shorttitle = {Inter-Subject {{Registration}} of {{Functional Images}}},
  author = {Dohmatob, Elvis and Varoquaux, Gael and Thirion, Bertrand},
  year = {2018},
  journal = {Frontiers in Neuroscience},
  volume = {12},
  issn = {1662-453X},
  doi = {10.3389/fnins.2018.00064},
  abstract = {In Echo-Planar Imaging (EPI)-based MRI (Magnetic Resonance Imaging), inter-subject registration typically uses the subject's T1-weighted (T1w) anatomical image to learn deformations of the subject's brain onto a template. The estimated deformation fields are then applied to the subject's EPI scans (functional or diffusion-weighted images) to warp the latter to a template space. Historically, such indirect T1w-based registration was motivated by the lack of clear anatomical details in low-resolution EPI images: a direct registration of the EPI scans to template space would be futile. A central prerequisite in such indirect methods is that the anatomical (aka the T1w) image of each subject is well aligned with their EPI images via rigid coregistration. We provide experimental evidence that things have changed: nowadays, there is a decent amount of anatomical contrast in high-resolution EPI data. That notwithstanding, EPI distortions due to B0 inhomogeneities cannot be fully corrected. Residual uncorrected distortions induce non-rigid deformations between the EPI scans and the same subject's anatomical scan. In this manuscript, we contribute a computationally cheap pipeline that leverages the high spatial resolution of modern EPI scans for direct inter-subject matching. Our pipeline is direct and does not rely on the T1w scan to estimate the inter-subject deformation. Results on a large dataset show that this new pipeline outperforms the classical indirect T1w-based registration scheme, across a variety of post-registration quality-assessment metrics including: Normalized Mutual Information, relative variance (variance-to-mean ratio), and to a lesser extent, improved peaks of group-level General Linear Model (GLM) activation maps.},
  langid = {english},
  keywords = {B0 inhomogeneity,Distortions,functional MRI,high-resolution EPI,human brain-mapping,inter-subject registration},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GQ94RS4Y\\Dohmatob et al_2018_Frontiers in Neuroscience_Inter-subject Registration of Functional Images.pdf}
}

@article{dolan_2008,
  title = {Neuroimaging of {{Cognition}}: {{Past}}, {{Present}}, and {{Future}}},
  shorttitle = {Neuroimaging of {{Cognition}}},
  author = {Dolan, R.J.},
  year = {2008},
  month = nov,
  journal = {Neuron},
  volume = {60},
  number = {3},
  pages = {496--502},
  issn = {08966273},
  doi = {10.1016/j.neuron.2008.10.038},
  langid = {english},
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}

@article{dolan_2013,
  title = {Goals and {{Habits}} in the {{Brain}}},
  author = {Dolan, Ray~J. and Dayan, Peter},
  year = {2013},
  month = oct,
  journal = {Neuron},
  volume = {80},
  number = {2},
  pages = {312--325},
  issn = {08966273},
  doi = {10.1016/j.neuron.2013.09.007},
  langid = {english},
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}

@article{doll_2015,
  title = {Model-Based Choices Involve Prospective Neural Activity},
  author = {Doll, Bradley B and Duncan, Katherine D and Simon, Dylan A and Shohamy, Daphna and Daw, Nathaniel D},
  year = {2015},
  journal = {Nature neuroscience},
  volume = {18},
  number = {5},
  pages = {767},
  annotation = {00000}
}

@article{donaldw.marquardt_,
  title = {An {{Algorithm}} for {{Least-Squares Estimation}} of {{Nonlinear Parameters}}},
  author = {Donald W. Marquardt},
  journal = {Journal of the Society for Industrial and Applied Mathematics},
  volume = {11},
  number = {2},
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}

@book{doring_2016,
  title = {{Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften}},
  author = {D{\"o}ring, Nicola and Bortz, J{\"u}rgen},
  year = {2016},
  series = {{Springer-Lehrbuch}},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-41089-5},
  isbn = {978-3-642-41088-8 978-3-642-41089-5},
  langid = {german},
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}

@book{doya_2007,
  title = {Bayesian Brain: Probabilistic Approaches to Neural Coding},
  shorttitle = {Bayesian Brain},
  editor = {Doya, Kenji},
  year = {2007},
  series = {Computational Neuroscience},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-04238-3},
  lccn = {QP376 .B39 2007},
  keywords = {Bayesian statistical decision theory,Brain,Neurons},
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}

@book{draper_1998,
  title = {Applied {{Regression Analysis}}},
  author = {Draper, Norman and Smith, Harry},
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}

@article{dreyfus_1962,
  title = {The Numerical Solution of Variational Problems},
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  year = {1962},
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  journal = {Journal of Mathematical Analysis and Applications},
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  number = {1},
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@article{drugowitsch_2012,
  title = {Probabilistic vs. Non-Probabilistic Approaches to the Neurobiology of Perceptual Decision-Making},
  author = {Drugowitsch, Jan and Pouget, Alexandre},
  year = {2012},
  month = dec,
  journal = {Current Opinion in Neurobiology},
  volume = {22},
  number = {6},
  pages = {963--969},
  issn = {09594388},
  doi = {10.1016/j.conb.2012.07.007},
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}

@article{drugowitsch_2012a,
  title = {The {{Cost}} of {{Accumulating Evidence}} in {{Perceptual Decision Making}}},
  author = {Drugowitsch, J. and {Moreno-Bote}, R. and Churchland, A. K. and Shadlen, M. N. and Pouget, A.},
  year = {2012},
  month = mar,
  journal = {Journal of Neuroscience},
  volume = {32},
  number = {11},
  pages = {3612--3628},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.4010-11.2012},
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}

@article{drugowitsch_2016,
  title = {Computational {{Precision}} of {{Mental Inference}} as {{Critical Source}} of {{Human Choice Suboptimality}}},
  author = {Drugowitsch, Jan and Wyart, Valentin and Devauchelle, Anne-Dominique and Koechlin, Etienne},
  year = {2016},
  month = dec,
  journal = {Neuron},
  volume = {92},
  number = {6},
  pages = {1398--1411},
  issn = {08966273},
  doi = {10.1016/j.neuron.2016.11.005},
  abstract = {Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments.},
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}

@article{du_,
  title = {Q-{{Learning}} for {{Bandit Problems}}},
  author = {Du, Michael O},
  pages = {32},
  abstract = {Multi-armed bandits may be viewed as decompositionally-structured Markov decision processes (MDP's) with potentially very large state sets. A particularly elegant methodology for computing optimal policies was developed over twenty ago by Gittins Gittins \& Jones, 1974]. Gittins' approach reduces the problem of nding optimal policies for the original MDP to a sequence of low-dimensional stopping problems whose solutions determine the optimal policy through the so-called \textbackslash Gittins indices." Katehakis and Veinott Katehakis \& Veinott, 1987] have shown that the Gittins index for a task in state i may be interpreted as a particular component of the maximum-value function associated with the \textbackslash restart-in-i" process, a simple MDP to which standard solution methods for computing optimal policies, such as successive approximation, apply. This paper explores the problem of learning the Gittins indices on-line without the aid of a process model; it suggests utilizing task-state-speci c Q-learning agents to solve their respective restart-in-state-i subproblems, and includes an example in which the online reinforcement learning approach is applied to a simple problem of stochastic scheduling|one instance drawn from a wide class of problems that may be formulated as bandit problems.},
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}

@book{duda_2001,
  title = {Pattern Classification},
  author = {Duda, Richard and Hart,, Peter and Stork, David},
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}

@article{dudoit_2003,
  title = {Multiple {{Hypothesis Testing}} in {{Microarray Experiments}}},
  author = {Dudoit, Sandrine and Shaffer, Juliet Popper and Block, Jennifer C.},
  year = {2003},
  month = feb,
  journal = {Statistical Science},
  volume = {18},
  number = {1},
  pages = {71--103},
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  doi = {10.1214/ss/1056397487},
  abstract = {DNA microarrays are part of a new and promising class of biotechnologies that allow the monitoring of expression levels in cells for thousands of genes simultaneously. An important and common question in DNA microarray experiments is the identification of differentially expressed genes, that is, genes whose expression levels are associated with a response or covariate of interest. The biological question of differential expression can be restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and the responses or covariates. As a typical microarray experiment measures expression levels for thousands of genes simultaneously, large multiplicity problems are generated. This article discusses different approaches to multiple hypothesis testing in the context of DNA microarray experiments and compares the procedures on microarray and simulated data sets.},
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@article{duhamel_1990,
  title = {Fast Fourier Transforms: {{A}} Tutorial Review and a State of the Art},
  shorttitle = {Fast Fourier Transforms},
  author = {Duhamel, P. and Vetterli, M.},
  year = {1990},
  month = apr,
  journal = {Signal Processing},
  volume = {19},
  number = {4},
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  doi = {10.1016/0165-1684(90)90158-U},
  abstract = {The publication of the Cooley-Tukey fast Fourier transform (FIT) algorithm in 1965 has opened a new area in digital signal processing by reducing the order of complexity of some crucial computational tasks like Fourier transform and convolution from N 2 to N log2 N, where N is the problem size. The development of the major algorithms (Cooley-Tukey and split-radix FFT, prime factor algorithm and Winograd fast Fourier transform) is reviewed. Then, an attempt is made to indicate the state of the art on the subject, showing the standing of research, open problems and implementations.},
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@book{duncan_1984,
  title = {Notes on Social Measurement: Historical and Critical},
  shorttitle = {Notes on Social Measurement},
  author = {Duncan, Otis Dudley},
  year = {1984},
  publisher = {{Russell Sage Foundation}},
  address = {{New York}},
  isbn = {978-0-87154-219-9},
  lccn = {HM253 .D86 1984},
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}

@article{duncan_2009,
  title = {Event-Related Potentials in Clinical Research: {{Guidelines}} for Eliciting, Recording, and Quantifying Mismatch Negativity, {{P300}}, and {{N400}}},
  shorttitle = {Event-Related Potentials in Clinical Research},
  author = {Duncan, Connie C. and Barry, Robert J. and Connolly, John F. and Fischer, Catherine and Michie, Patricia T. and N{\"a}{\"a}t{\"a}nen, Risto and Polich, John and Reinvang, Ivar and Van Petten, Cyma},
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}

@article{dunlap_,
  title = {Meta-{{Analysis}} of {{Experiments With Matched Groups}} or {{Repeated Measures Designs}}},
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@article{durnez_2014,
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  author = {Durnez, Joke and Moerkerke, Beatrijs and Nichols, Thomas E.},
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@article{durnez_2016,
  ids = {durnez_power_2016},
  title = {Power and Sample Size Calculations for {{fMRI}} Studies Based on the Prevalence of Active Peaks.},
  author = {Durnez, Joke and Degryse, Jasper and Moerkerke, Beatrijs and Seurinck, Ruth and Sochat, Vanessa and Poldrack, Russell A and Nichols, Thomas E},
  year = {2016},
  pages = {24},
  abstract = {Mounting evidence over the last few years suggest that published neuroscience research suffer from low power, and especially for published fMRI experiments. Not only does low power decrease the chance of detecting a true effect, it also reduces the chance that a statistically significant result indicates a true effect (Ioannidis, 2005). Put another way, findings with the least power will be the least reproducible, and thus a (prospective) power analysis is a critical component of any paper. In this work we present a simple way to characterize the spatial signal in a fMRI study with just two parameters, and a direct way to estimate these two parameters based on an existing study. Specifically, using just (1) the proportion of the brain activated and (2) the average effect size in activated brain regions, we can produce closed form power calculations for given sample size, brain volume and smoothness. This procedure allows one to minimize the cost of an fMRI experiment, while preserving a predefined statistical power. The method is evaluated and illustrated using simulations and real neuroimaging data from the Human Connectome Project. The procedures presented in this paper are made publicly available in an online web-based toolbox available at www.neuropowertools.org.},
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}

@inproceedings{durrieu_2012,
  title = {Lower and Upper Bounds for Approximation of the {{Kullback-Leibler}} Divergence between {{Gaussian Mixture Models}}},
  booktitle = {2012 {{IEEE International Conference}} on {{Acoustics}}, {{Speech}} and {{Signal Processing}} ({{ICASSP}})},
  author = {Durrieu, J.-L. and Thiran, J.-Ph. and Kelly, F.},
  year = {2012},
  month = mar,
  pages = {4833--4836},
  publisher = {{IEEE}},
  address = {{Kyoto, Japan}},
  doi = {10.1109/ICASSP.2012.6289001},
  abstract = {Many speech technology systems rely on Gaussian Mixture Models (GMMs). The need for a comparison between two GMMs arises in applications such as speaker verification, model selection or parameter estimation. For this purpose, the Kullback-Leibler (KL) divergence is often used. However, since there is no closed form expression to compute it, it can only be approximated. We propose lower and upper bounds for the KL divergence, which lead to a new approximation and interesting insights into previously proposed approximations. An application to the comparison of speaker models also shows how such approximations can be used to validate assumptions on the models.},
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}

@article{dwyer_2018,
  title = {Machine {{Learning Approaches}} for {{Clinical Psychology}} and {{Psychiatry}}},
  author = {Dwyer, Dominic B. and Falkai, Peter and Koutsouleris, Nikolaos},
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  abstract = {Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice because of its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to the critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.},
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@article{eckersley_2003a,
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  author = {Eckersley, Peter and Egan, Gary F. and De Schutter, Erik and Yiyuan, Tang and Novak, Mirko and Sebesta, Vaclav and Matthiessen, Line and Jaaskelainen, Irio P. and Ruotsalainen, Ulla and Herz, Andreas V. M. and Hoffmann, Klaus-Peter and Ritz, Raphael and Ravindranath, Viji and Beltrame, Francesco and Amari, Shun-ichi and Usui, Shiro and Lee, Soo-Young and {van Pelt}, Jaap and Bjaalie, Jan G. and Wrobel, Andrzej and {da Silva}, Fernando Mira and Gonzalez, Carmen and Grillner, Sten and Verschure, Paul and Dalkara, Turgay and Bennett, Rob and Willshaw, David and Koslow, Stephen H. and Miller, Perry L. and Subramaniam, Shankar and Toga, Arthur W.},
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@article{edelson_2018,
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@article{editorial_2018,
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@book{efron_2016,
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@article{eklund_2012,
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@article{eklund_2016,
  title = {Cluster Failure: {{Why fMRI}} Inferences for Spatial Extent Have Inflated False-Positive Rates},
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@article{eklund_2017,
  title = {Reply to {{Brown}} and {{Behrmann}}, {{Cox}}, et al., and {{Kessler}} et al.: {{Data}} and Code Sharing Is the Way Forward for {{fMRI}}},
  shorttitle = {Reply to {{Brown}} and {{Behrmann}}, {{Cox}}, et al., and {{Kessler}} et Al.},
  author = {Eklund, Anders and Nichols, Thomas E. and Knutsson, Hans},
  year = {2017},
  month = apr,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {114},
  number = {17},
  pages = {E3374-E3375},
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  doi = {10.1073/pnas.1620285114},
  abstract = {We are glad that our paper (1) has generated intense discussions in the fMRI field (2{$\Downarrow$}\textendash 4), on how to analyze fMRI data, and how to correct for multiple comparisons. The goal of the paper was not to disparage any specific fMRI software, but to point out that parametric statistical methods are based on a number of assumptions that are not always valid for fMRI data, and that nonparametric statistical methods (5) are a good alternative. Through AFNI's introduction of nonparametric statistics in the function 3dttest++ (3, 6), the three most common fMRI softwares now all support nonparametric group inference [SPM through the toolbox SnPM (www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/software/snpm), and FSL through the function randomise]. Cox et al. (3) correctly point out that the bug in the AFNI function 3dClustSim only had a minor impact on the false-positive rate (FPR). This was also covered in our original paper (1): ``We note that FWE [familywise error] rates are lower with the bug-fixed 3dClustSim function. As an example, the updated function reduces the degree of false \ldots{}  [{$\carriagereturn$}][1]1To whom correspondence should be addressed. Email: anders.eklund\{at\}liu.se.  [1]: \#xref-corresp-1-1},
  langid = {english},
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}

@article{eklund_2018,
  title = {Cluster {{Failure Revisited}}: {{Impact}} of {{First Level Design}} and {{Data Quality}} on {{Cluster False Positive Rates}}},
  shorttitle = {Cluster {{Failure Revisited}}},
  author = {Eklund, Anders and Knutsson, Hans and Nichols, Thomas E.},
  year = {2018},
  month = apr,
  journal = {arXiv:1804.03185 [stat]},
  eprint = {1804.03185},
  eprinttype = {arxiv},
  primaryclass = {stat},
  abstract = {Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomisation of events between subjects. We consider the lack of validity found with onesample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all datasets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10\% of the fMRI studies have used the most problematic cluster inference method (P = 0.01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Statistics - Applications},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JCY2MKDS\\Eklund et al. - 2018 - Cluster Failure Revisited Impact of First Level D.pdf}
}

@article{eklund_2018a,
  title = {Reply to {{Chen}} et al.: {{Parametric}} Methods for Cluster Inference Perform Worse for Two-sided {\emph{t}} -tests},
  shorttitle = {Reply to {{Chen}} et Al.},
  author = {Eklund, Anders and Knutsson, Hans and Nichols, Thomas E.},
  year = {2018},
  month = dec,
  journal = {Human Brain Mapping},
  issn = {1065-9471, 1097-0193},
  doi = {10.1002/hbm.24465},
  abstract = {One-sided t-tests are commonly used in the neuroimaging field, but two-sided tests should be the default unless a researcher has a strong reason for using a one-sided test. Here we extend our previous work on cluster false positive rates, which used one-sided tests, to two-sided tests. Briefly, we found that parametric methods perform worse for two-sided t-tests, and that nonparametric methods perform equally well for one-sided and two-sided tests.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5U83SH9Y\\Eklund et al. - 2018 - Reply to Chen et al. Parametric methods for clust.pdf}
}

@article{eklund_2019,
  title = {Cluster Failure Revisited: {{Impact}} of First Level Design and Physiological Noise on Cluster False Positive Rates},
  shorttitle = {Cluster Failure Revisited},
  author = {Eklund, Anders and Knutsson, Hans and Nichols, Thomas E.},
  year = {2019},
  month = may,
  journal = {Human Brain Mapping},
  volume = {40},
  number = {7},
  pages = {2017--2032},
  issn = {1065-9471, 1097-0193},
  doi = {10.1002/hbm.24350},
  abstract = {Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10\% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.},
  langid = {english},
  annotation = {00004},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TSSWIFGG\\Eklund et al. - 2019 - Cluster failure revisited Impact of first level d.pdf}
}

@article{ellis_2019,
  title = {{Einf\"uhrung in die Programmierung mit R}},
  author = {Ellis, Andrew and Mayer, Boris},
  year = {2019},
  pages = {220},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4XT6MUPU\\Ellis und Mayer - 2019 - Einführung in die Programmierung mit R.pdf}
}

@article{engel_2010,
  title = {Beta-Band Oscillations\textemdash Signalling the Status Quo?},
  author = {Engel, Andreas K and Fries, Pascal},
  year = {2010},
  month = apr,
  journal = {Current Opinion in Neurobiology},
  volume = {20},
  number = {2},
  pages = {156--165},
  issn = {09594388},
  doi = {10.1016/j.conb.2010.02.015},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NHAJFH38\\Engel_Fries_2010_Current Opinion in Neurobiology_Beta-band oscillations—signalling the status quo.pdf}
}

@book{epping_2015,
  title = {Grundrechte},
  author = {Epping, Volker and Lenz, Sebastian and Leydecker, Philipp},
  year = {2015},
  series = {Springer-{{Lehrbuch}}},
  edition = {6. Auflage},
  publisher = {{Springer Berlin}},
  address = {{Heidelberg : Dordrecht}},
  isbn = {978-3-642-54657-0},
  lccn = {KK5132 .E67 2015},
  keywords = {Civil rights,Germany},
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}

@article{eppinger_2011,
  title = {To {{Choose}} or to {{Avoid}}: {{Age Differences}} in {{Learning}} from {{Positive}} and {{Negative Feedback}}},
  shorttitle = {To {{Choose}} or to {{Avoid}}},
  author = {Eppinger, Ben and Kray, Jutta},
  year = {2011},
  month = jan,
  journal = {Journal of Cognitive Neuroscience},
  volume = {23},
  number = {1},
  pages = {41--52},
  issn = {0898-929X, 1530-8898},
  doi = {10.1162/jocn.2009.21364},
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  annotation = {00053},
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}

@article{eppinger_2013,
  title = {Reduced {{Striatal Responses}} to {{Reward Prediction Errors}} in {{Older Compared}} with {{Younger Adults}}},
  author = {Eppinger, B. and Schuck, N. W. and Nystrom, L. E. and Cohen, J. D.},
  year = {2013},
  month = jun,
  journal = {Journal of Neuroscience},
  volume = {33},
  number = {24},
  pages = {9905--9912},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.2942-12.2013},
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}

@article{epstein_1998,
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@book{epstein_2008,
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  author = {Epstein, Charles L.},
  year = {2008},
  edition = {2nd ed},
  publisher = {{Society for Industrial and Applied Mathematics}},
  address = {{Philadelphia, PA}},
  isbn = {978-0-89871-642-9},
  langid = {english},
  lccn = {RC78.7.D53 E676 2008},
  keywords = {Diagnostic imaging,Imaging systems in medicine,Mathematics,Medical physics},
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@article{erev_2010,
  title = {A Choice Prediction Competition: {{Choices}} from Experience and from Description},
  shorttitle = {A Choice Prediction Competition},
  author = {Erev, Ido and Ert, Eyal and Roth, Alvin E. and Haruvy, Ernan and Herzog, Stefan M. and Hau, Robin and Hertwig, Ralph and Stewart, Terrence and West, Robert and Lebiere, Christian},
  year = {2010},
  month = jan,
  journal = {Journal of Behavioral Decision Making},
  volume = {23},
  number = {1},
  pages = {15--47},
  issn = {08943257, 10990771},
  doi = {10.1002/bdm.683},
  langid = {english},
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}

@article{erren_2007,
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  author = {Erren, Thomas C. and Bourne, Philip E.},
  year = {2007},
  journal = {PLoS Computational Biology},
  volume = {3},
  number = {5},
  pages = {e102},
  issn = {1553-734X, 1553-7358},
  doi = {10.1371/journal.pcbi.0030102},
  langid = {english},
  annotation = {00086},
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}

@article{esposito_2009,
  title = {Combined Distributed Source and Single-Trial {{EEG}}\textendash{{fMRI}} Modeling: {{Application}} to Effortful Decision Making Processes},
  shorttitle = {Combined Distributed Source and Single-Trial {{EEG}}\textendash{{fMRI}} Modeling},
  author = {Esposito, Fabrizio and Mulert, Christoph and Goebel, Rainer},
  year = {2009},
  month = aug,
  journal = {NeuroImage},
  volume = {47},
  number = {1},
  pages = {112--121},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.03.074},
  langid = {english},
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}

@article{esposito_2009a,
  title = {Distributed Analysis of Simultaneous {{EEG-fMRI}} Time-Series: Modeling and Interpretation Issues},
  shorttitle = {Distributed Analysis of Simultaneous {{EEG-fMRI}} Time-Series},
  author = {Esposito, Fabrizio and Aragri, Adriana and Piccoli, Tommaso and Tedeschi, Gioacchino and Goebel, Rainer and Di Salle, Francesco},
  year = {2009},
  month = oct,
  journal = {Magnetic Resonance Imaging},
  volume = {27},
  number = {8},
  pages = {1120--1130},
  issn = {0730725X},
  doi = {10.1016/j.mri.2009.01.007},
  langid = {english},
  annotation = {00000},
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}

@article{europeansocietyofradiologyesr_2017,
  title = {The New {{EU General Data Protection Regulation}}: What the Radiologist Should Know},
  shorttitle = {The New {{EU General Data Protection Regulation}}},
  author = {{European Society of Radiology (ESR)}},
  year = {2017},
  month = jun,
  journal = {Insights into Imaging},
  volume = {8},
  number = {3},
  pages = {295--299},
  issn = {1869-4101},
  doi = {10.1007/s13244-017-0552-7},
  langid = {english},
  annotation = {00000},
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}

@article{evans_,
  title = {Methods for Approximating},
  author = {Evans},
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}

@article{even-dar_,
  ids = {evendar2006},
  title = {Action {{Elimination}} and {{Stopping Conditions}} for the {{Multi-Armed Bandit}} and {{Reinforcement Learning Problems}}},
  author = {{Even-Dar}, Eyal and Mannor, Shie and Mansour, Yishay},
  pages = {27},
  abstract = {We incorporate statistical confidence intervals in both the multi-armed bandit and the reinforcement learning problems. In the bandit problem we show that given n arms, it suffices to pull the arms a total of O (n/{$\epsilon$}2) log(1/{$\delta$}) times to find an {$\epsilon$}-optimal arm with probability of at least 1 - {$\delta$}. This bound matches the lower bound of Mannor and Tsitsiklis (2004) up to constants. We also devise action elimination procedures in reinforcement learning algorithms. We describe a framework that is based on learning the confidence interval around the value function or the Q-function and eliminating actions that are not optimal (with high probability). We provide a model-based and a model-free variants of the elimination method. We further derive stopping conditions guaranteeing that the learned policy is approximately optimal with high probability. Simulations demonstrate a considerable speedup and added robustness over {$\epsilon$}-greedy Q-learning.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KYLY5MZL\\Even-Dar et al. - Action Elimination and Stopping Conditions for the.pdf;C\:\\Users\\dirko\\Zotero\\storage\\M8H7P298\\Even-Dar et al. - Action Elimination and Stopping Conditions for the.pdf}
}

@book{evensen_2007,
  title = {Data Assimilation: The Ensemble {{Kalman}} Filter},
  shorttitle = {Data Assimilation},
  author = {Evensen, Geir},
  year = {2007},
  publisher = {{Springer}},
  address = {{Berlin ; New York}},
  isbn = {978-3-540-38300-0},
  lccn = {QA274 .E88 2007},
  keywords = {Kalman filtering,Stochastic processes},
  annotation = {00000  OCLC: ocm74270320},
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}

@book{everitt_1984,
  title = {An {{Introduction}} to {{Latent Variable Models}}},
  author = {Everitt, B. S.},
  year = {1984},
  publisher = {{Springer Netherlands}},
  address = {{Dordrecht}},
  doi = {10.1007/978-94-009-5564-6},
  isbn = {978-94-010-8954-8 978-94-009-5564-6},
  langid = {english},
  annotation = {00002},
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@article{eyink_1996,
  title = {Action Principle in Nonequilibrium Statistical Dynamics},
  author = {Eyink, Gregory L.},
  year = {1996},
  month = oct,
  journal = {Physical Review E},
  volume = {54},
  number = {4},
  pages = {3419--3435},
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  doi = {10.1103/PhysRevE.54.3419},
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}

@book{eynden_2011,
  title = {Managing and Sharing Data: A Best Practice Guide for Researchers},
  shorttitle = {Managing and Sharing Data},
  author = {van den Eynden, Veerle and {UK Data Archive}},
  year = {2011},
  publisher = {{UK Data Archive}},
  address = {{Colchester}},
  isbn = {978-1-904059-78-3},
  langid = {english},
  annotation = {OCLC: 727970452},
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}

@book{fahrmeir_2001,
  title = {Multivariate {{Statistical Modelling Based}} on {{Generalized Linear Models}}},
  author = {Fahrmeir, Ludwig and Tutz, Gerhard},
  year = {2001},
  series = {Springer {{Series}} in {{Statistics}}},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4757-3454-6},
  isbn = {978-1-4419-2900-6 978-1-4757-3454-6},
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}

@book{fahrmeir_2016,
  title = {{Statistik}},
  author = {Fahrmeir, Ludwig and Heumann, Christian and K{\"u}nstler, Rita and Pigeot, Iris and Tutz, Gerhard},
  year = {2016},
  series = {{Springer-Lehrbuch}},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-662-50372-0},
  isbn = {978-3-662-50371-3 978-3-662-50372-0},
  langid = {german},
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}

@book{fahrmeir_2016a,
  title = {{Statistik}},
  author = {Fahrmeir, Ludwig and Heumann, Christian and K{\"u}nstler, Rita and Pigeot, Iris and Tutz, Gerhard},
  year = {2016},
  series = {{Springer-Lehrbuch}},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-662-50372-0},
  isbn = {978-3-662-50371-3 978-3-662-50372-0},
  langid = {german},
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}

@article{fallani_2010,
  title = {A Graph-Theoretical Approach in Brain Functional Networks. {{Possible}} Implications in {{EEG}} Studies},
  author = {Fallani, Fabrizio and Costa, Luciano and Rodriguez, Francisco and Astolfi, Laura and Vecchiato, Giovanni and Toppi, Jlenia and Borghini, Gianluca and Cincotti, Febo and Mattia, Donatella and Salinari, Serenella and Isabella, Roberto and Babiloni, Fabio},
  year = {2010},
  journal = {Nonlinear Biomedical Physics},
  volume = {4},
  number = {Suppl 1},
  pages = {S8},
  issn = {1753-4631},
  doi = {10.1186/1753-4631-4-S1-S8},
  abstract = {Background: Recently, it was realized that the functional connectivity networks estimated from actual brainimaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods: We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results: Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ``hubs'' for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of ``activation'' {$\Omega$} within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions: The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.},
  langid = {english},
  annotation = {00027},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NH9EYQPB\\Fallani et al. - 2010 - A graph-theoretical approach in brain functional n.pdf}
}

@article{falmagne_1980,
  title = {A {{Probabilistic Theory}} of {{Extensive Measurement}}},
  author = {Falmagne, Jean-Claude},
  year = {1980},
  month = jun,
  journal = {Philosophy of Science},
  volume = {47},
  number = {2},
  pages = {277--296},
  issn = {0031-8248, 1539-767X},
  doi = {10.1086/288933},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BTF2JE5L\\Falmagne - 1980 - A Probabilistic Theory of Extensive Measurement.pdf}
}

@article{fan_2005,
  title = {The Activation of Attentional Networks},
  author = {Fan, Jin and McCandliss, Bruce D. and Fossella, John and Flombaum, Jonathan I. and Posner, Michael I.},
  year = {2005},
  month = jun,
  journal = {NeuroImage},
  volume = {26},
  number = {2},
  pages = {471--479},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2005.02.004},
  abstract = {Alerting, orienting, and executive control are widely thought to be relatively independent aspects of attention that are linked to separable brain regions. However, neuroimaging studies have yet to examine evidence for the anatomical separability of these three aspects of attention in the same subjects performing the same task. The attention network test (ANT) examines the effects of cues and targets within a single reaction time task to provide a means of exploring the efficiency of the alerting, orienting, and executive control networks involved in attention. It also provides an opportunity to examine the brain activity of these three networks as they operate in a single integrated task. We used event-related functional magnetic resonance imaging (fMRI) to explore the brain areas involved in the three attention systems targeted by the ANT. The alerting contrast showed strong thalamic involvement and activation of anterior and posterior cortical sites. As expected, the orienting contrast activated parietal sites and frontal eye fields. The executive control network contrast showed activation of the anterior cingulate along with several other brain areas. With some exceptions, activation patterns of these three networks within this single task are consistent with previous fMRI studies that have been studied in separate tasks. Overall, the fMRI results suggest that the functional contrasts within this single task differentially activate three separable anatomical networks related to the components of attention.},
  keywords = {Alerting,Attention,Attentional networks,Executive control,fMRI,Orienting},
  annotation = {01294},
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}

@article{faraji_2018,
  title = {Balancing {{New}} against {{Old Information}}: {{The Role}} of {{Puzzlement Surprise}} in {{Learning}}},
  shorttitle = {Balancing {{New}} against {{Old Information}}},
  author = {Faraji, Mohammadjavad and Preuschoff, Kerstin and Gerstner, Wulfram},
  year = {2018},
  month = jan,
  journal = {Neural Computation},
  volume = {30},
  number = {1},
  pages = {34--83},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/neco_a_01025},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QCJ9XXNG\\Faraji et al_2018_Neural Computation_Balancing New against Old Information.pdf}
}

@article{farashahi_2017,
  title = {Feature-Based Learning Improves Adaptability without Compromising Precision},
  author = {Farashahi, Shiva and Rowe, Katherine and Aslami, Zohra and Lee, Daeyeol and Soltani, Alireza},
  year = {2017},
  month = dec,
  journal = {Nature Communications},
  volume = {8},
  number = {1},
  issn = {2041-1723},
  doi = {10.1038/s41467-017-01874-w},
  langid = {english},
  annotation = {00002},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZZ72JBS3\\Farashahi et al. - 2017 - Feature-based learning improves adaptability witho.pdf}
}

@article{farrar_1967,
  title = {Multicollinearity in {{Regression Analysis}}: {{The Problem Revisited}}},
  shorttitle = {Multicollinearity in {{Regression Analysis}}},
  author = {Farrar, Donald E. and Glauber, Robert R.},
  year = {1967},
  month = feb,
  journal = {The Review of Economics and Statistics},
  volume = {49},
  number = {1},
  pages = {92},
  issn = {00346535},
  doi = {10.2307/1937887},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5SB2PLN7\\Farrar_Glauber_1967_The Review of Economics and Statistics_Multicollinearity in Regression Analysis.pdf}
}

@book{farrar_1971,
  title = {Pulse and {{Fourier Transform NMR}}. {{Introduction}} to {{Theory}} and {{Methods}}},
  author = {Farrar, Thomas and Becker},
  year = {1971},
  annotation = {02454},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9UHSBQIN\\Thomas C. Farrar and Edwin D. Becker (Auth.) - Pulse and Fourier Transform NMR. Introduction to Theory and Methods (1971, New York, Academic Press).pdf}
}

@book{farrell_2018,
  title = {Computational {{Modeling}} of {{Cognition}} and {{Behavior}}:},
  shorttitle = {Computational {{Modeling}} of {{Cognition}} and {{Behavior}}},
  author = {Farrell, Simon and Lewandowsky, Stephan},
  year = {2018},
  month = feb,
  edition = {First},
  publisher = {{Cambridge University Press}},
  doi = {10.1017/CBO9781316272503},
  isbn = {978-1-107-10999-5 978-1-316-27250-3 978-1-107-52561-0},
  langid = {english},
  annotation = {00019},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\F6EB95PS\\Farrell und Lewandowsky - 2018 - Computational Modeling of Cognition and Behavior.pdf}
}

@article{fastenrath_2009,
  title = {Dynamical Causal Modelling for {{M}}/{{EEG}}: {{Spatial}} and Temporal Symmetry Constraints},
  shorttitle = {Dynamical Causal Modelling for {{M}}/{{EEG}}},
  author = {Fastenrath, M and Friston, K and Kiebel, S},
  year = {2009},
  month = jan,
  journal = {NeuroImage},
  volume = {44},
  number = {1},
  pages = {154--163},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.07.041},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GMZCPHUP\\Fastenrath et al_2009_NeuroImage_Dynamical causal modelling for M-EEG.pdf;C\:\\Users\\dirko\\Zotero\\storage\\KXU6EWHQ\\Fastenrath et al_2009_NeuroImage_Dynamical causal modelling for M-EEG.pdf}
}

@article{faugeras_2009,
  title = {A Constructive Mean-Field Analysis of Multi Population Neural Networks with Random Synaptic Weights and Stochastic Inputs},
  author = {Faugeras, Olivier},
  year = {2009},
  journal = {Frontiers in Computational Neuroscience},
  volume = {3},
  issn = {16625188},
  doi = {10.3389/neuro.10.001.2009},
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}

@article{faul_2007,
  title = {G*{{Power}} 3: {{A}} Flexible Statistical Power Analysis Program for the Social, Behavioral, and Biomedical Sciences},
  shorttitle = {G*{{Power}} 3},
  author = {Faul, Franz and Erdfelder, Edgar and Lang, Albert-Georg and Buchner, Axel},
  year = {2007},
  month = may,
  journal = {Behavior Research Methods},
  volume = {39},
  number = {2},
  pages = {175--191},
  issn = {1554-351X, 1554-3528},
  doi = {10.3758/BF03193146},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TAXYN38A\\Faul et al. - 2007 - GPower 3 A flexible statistical power analysis p.pdf}
}

@article{faul_2009,
  title = {Statistical Power Analyses Using {{G}}*{{Power}} 3.1: {{Tests}} for Correlation and Regression Analyses},
  shorttitle = {Statistical Power Analyses Using {{G}}*{{Power}} 3.1},
  author = {Faul, Franz and Erdfelder, Edgar and Buchner, Axel and Lang, Albert-Georg},
  year = {2009},
  month = nov,
  journal = {Behavior Research Methods},
  volume = {41},
  number = {4},
  pages = {1149--1160},
  issn = {1554-351X, 1554-3528},
  doi = {10.3758/BRM.41.4.1149},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CP79MAK9\\Faul et al. - 2009 - Statistical power analyses using GPower 3.1 Test.pdf}
}

@article{fecher_2015,
  title = {What {{Drives Academic Data Sharing}}?},
  author = {Fecher, Benedikt and Friesike, Sascha and Hebing, Marcel},
  editor = {Phillips, Robert S.},
  year = {2015},
  month = feb,
  journal = {PLOS ONE},
  volume = {10},
  number = {2},
  pages = {e0118053},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0118053},
  langid = {english},
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}

@article{feise_2002,
  title = {Do Multiple Outcome Measures Require P-Value Adjustment?},
  author = {Feise, Ronald J},
  year = {2002},
  month = dec,
  journal = {BMC Medical Research Methodology},
  volume = {2},
  number = {1},
  issn = {1471-2288},
  doi = {10.1186/1471-2288-2-8},
  abstract = {Background: Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used.},
  langid = {english},
  annotation = {00610},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Y5Q4ZG8L\\Feise - 2002 - Do multiple outcome measures require p-value adjus.pdf}
}

@article{feldman_,
  title = {{{http://hornacek.coa.edu/dave/}}},
  author = {Feldman, David},
  pages = {49},
  annotation = {00000},
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}

@book{field_2012,
  title = {Discovering Statistics Using {{R}}},
  author = {Field, Andy P. and Miles, Jeremy and Field, Zo{\"e}},
  year = {2012},
  publisher = {{Sage}},
  address = {{London ; Thousand Oaks, Calif}},
  isbn = {978-1-4462-0046-9 978-1-4462-0045-2},
  langid = {english},
  lccn = {HA32 .F537 2012},
  keywords = {Computer programs,R (Computer program language),Social sciences,Statistical methods Computer programs,Statistics},
  annotation = {OCLC: ocn760970657},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\F2VMAUAV\\Field et al. - 2012 - Discovering statistics using R.pdf}
}

@book{field_2012a,
  title = {Discovering Statistics Using {{R}}},
  author = {Field, Andy P. and Miles, Jeremy and Field, Zo{\"e}},
  year = {2012},
  publisher = {{Sage}},
  address = {{London ; Thousand Oaks, Calif}},
  isbn = {978-1-4462-0046-9 978-1-4462-0045-2},
  langid = {english},
  lccn = {HA32 .F537 2012},
  keywords = {Computer programs,R (Computer program language),Social sciences,Statistical methods Computer programs,Statistics},
  annotation = {OCLC: ocn760970657},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6YBKCJD3\\Field et al. - 2012 - Discovering statistics using R.pdf}
}

@article{fienberg_2006,
  title = {When Did {{Bayesian}} Inference Become "{{Bayesian}}"?},
  author = {Fienberg, Stephen E.},
  year = {2006},
  month = mar,
  journal = {Bayesian Analysis},
  volume = {1},
  number = {1},
  pages = {1--40},
  issn = {1936-0975},
  doi = {10.1214/06-BA101},
  abstract = {While Bayes' theorem has a 250-year history, and the method of inverse probability that flowed from it dominated statistical thinking into the twentieth century, the adjective ``Bayesian'' was not part of the statistical lexicon until relatively recently. This paper provides an overview of key Bayesian developments, beginning with Bayes' posthumously published 1763 paper and continuing up through approximately 1970, including the period of time when ``Bayesian'' emerged as the label of choice for those who advocated Bayesian methods.},
  langid = {english},
  annotation = {00213},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GEYZH99U\\Fienberg_2006_Bayesian Analysis_When did Bayesian inference become Bayesian.pdf}
}

@article{findling_2019,
  title = {Computational Noise in Reward-Guided Learning Drives Behavioral Variability in Volatile Environments},
  author = {Findling, Charles and Skvortsova, Vasilisa and Dromnelle, R{\'e}mi and Palminteri, Stefano and Wyart, Valentin},
  year = {2019},
  month = dec,
  journal = {Nature Neuroscience},
  volume = {22},
  number = {12},
  pages = {2066--2077},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-019-0518-9},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\X75APVK6\\Findling et al. - 2019 - Computational noise in reward-guided learning driv.pdf}
}

@article{fine_,
  title = {The {{Hierarchical Hidden Markov Model}}: {{Analysis}} and {{Applications}}},
  author = {FINE, SHAI},
  pages = {22},
  abstract = {We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our model is motivated by the complex multi-scale structure which appears in many natural sequences, particularly in language, handwriting and speech. We seek a systematic unsupervised approach to the modeling of such structures. By extending the standard Baum-Welch (forward-backward) algorithm, we derive an efficient procedure for estimating the model parameters from unlabeled data. We then use the trained model for automatic hierarchical parsing of observation sequences. We describe two applications of our model and its parameter estimation procedure. In the first application we show how to construct hierarchical models of natural English text. In these models different levels of the hierarchy correspond to structures on different length scales in the text. In the second application we demonstrate how HHMMs can be used to automatically identify repeated strokes that represent combination of letters in cursive handwriting.},
  annotation = {00990},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RDWU6PKI\\FINE - The Hierarchical Hidden Markov Model Analysis and.pdf;C\:\\Users\\dirko\\Zotero\\storage\\XA5VHG8D\\FINE - The Hierarchical Hidden Markov Model Analysis and.pdf}
}

@article{finn_2015,
  title = {Functional Connectome Fingerprinting: Identifying Individuals Using Patterns of Brain Connectivity},
  shorttitle = {Functional Connectome Fingerprinting},
  author = {Finn, Emily S and Shen, Xilin and Scheinost, Dustin and Rosenberg, Monica D and Huang, Jessica and Chun, Marvin M and Papademetris, Xenophon and Constable, R Todd},
  year = {2015},
  month = nov,
  journal = {Nature Neuroscience},
  volume = {18},
  number = {11},
  pages = {1664--1671},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.4135},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2VAPNIY4\\Finn et al. - 2015 - Functional connectome fingerprinting identifying .pdf;C\:\\Users\\dirko\\Zotero\\storage\\CENXVS8H\\Finn, Shen et al 2015 - Functional connectome fingerprinting.pdf;C\:\\Users\\dirko\\Zotero\\storage\\EAX73JR9\\Finn et al. - 2015 - Functional connectome fingerprinting identifying .pdf}
}

@article{finn_2018,
  title = {Layer-Dependent Activity in Human Prefrontal Cortex during Working Memory},
  author = {Finn, Emily S. and Huber, Laurentius and Jangraw, David C. and Bandettini, Peter A.},
  year = {2018},
  month = sep,
  doi = {10.1101/425249},
  abstract = {Working memory involves a series of functions: encoding a stimulus, maintaining or manipulating its representation over a delay, and finally making a behavioral response. While working memory engages dorsolateral prefrontal cortex (dlPFC), few studies have investigated whether these subfunctions are localized to different cortical depths in this region, and none have done so in humans. Here, we use high-resolution functional MRI to interrogate the layer specificity of neural activity during different epochs of a working memory task in dlPFC. We detect activity timecourses that follow the hypothesized patterns: superficial layers are preferentially active during the delay period, while deeper layers are preferentially active during the response. Results demonstrate that layer-specific fMRI can be used in higher-order brain regions to non-invasively map cognitive information processing along cortical circuitry in humans.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RWYSMYYV\\Finn et al. - 2018 - Layer-dependent activity in human prefrontal corte.pdf}
}

@article{fiorillo_2012,
  title = {Beyond {{Bayes}}: {{On}} the {{Need}} for a {{Unified}} and {{Jaynesian Definition}} of {{Probability}} and {{Information}} within {{Neuroscience}}},
  shorttitle = {Beyond {{Bayes}}},
  author = {Fiorillo, Christopher D.},
  year = {2012},
  month = apr,
  journal = {Information},
  volume = {3},
  number = {4},
  pages = {175--203},
  issn = {2078-2489},
  doi = {10.3390/info3020175},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\76SZK2JA\\Fiorillo_2012_Information_Beyond Bayes.pdf}
}

@article{fischer_2017,
  title = {Short-Term Reward Experience Biases Inference despite Dissociable Neural Correlates},
  author = {Fischer, Adrian G and {Bourgeois-Gironde}, Sacha and Ullsperger, Markus},
  year = {2017},
  journal = {Nature communications},
  volume = {8},
  number = {1},
  pages = {1690},
  annotation = {00000}
}

@book{fisher_1925,
  title = {Statistical {{Methods}} for {{Research Workers}}},
  author = {Fisher, R. A.},
  year = {1925},
  publisher = {{Oliver \& Boyd}},
  langid = {english},
  annotation = {23254},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\85EL3PPV\\Fisher - 1925 - Statistical Methods for Research Workers.pdf}
}

@article{fisher_1936,
  title = {The Use of Multiple Measurements in Taxonomic Problems},
  author = {Fisher, R. A.},
  year = {1936},
  month = sep,
  journal = {Annals of Eugenics},
  volume = {7},
  number = {2},
  pages = {179--188},
  issn = {20501420},
  doi = {10.1111/j.1469-1809.1936.tb02137.x},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2GDAKCPQ\\Fisher - 1936 - The use of multiple measurements in taxonomic prob.pdf}
}

@article{fitzgerald_2010,
  title = {Differentiable {{Neural Substrates}} for {{Learned}} and {{Described Value}} and {{Risk}}},
  author = {FitzGerald, Thomas H.B. and Seymour, Ben and Bach, Dominik R. and Dolan, Raymond J.},
  year = {2010},
  month = oct,
  journal = {Current Biology},
  volume = {20},
  number = {20},
  pages = {1823--1829},
  issn = {09609822},
  doi = {10.1016/j.cub.2010.08.048},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HNCMXG5G\\FitzGerald et al_2010_Current Biology_Differentiable Neural Substrates for Learned and Described Value and Risk.pdf}
}

@article{fitzgerald_2015,
  title = {Precision and Neuronal Dynamics in the Human Posterior Parietal Cortex during Evidence Accumulation},
  author = {FitzGerald, Thomas H.B. and Moran, Rosalyn J. and Friston, Karl J. and Dolan, Raymond J.},
  year = {2015},
  month = feb,
  journal = {NeuroImage},
  volume = {107},
  pages = {219--228},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2014.12.015},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\W32CFGG3\\FitzGerald et al_2015_NeuroImage_Precision and neuronal dynamics in the human posterior parietal cortex during.pdf;C\:\\Users\\dirko\\Zotero\\storage\\X4DJ3CV9\\FitzGerald et al_2015_NeuroImage_Precision and neuronal dynamics in the human posterior parietal cortex during.pdf}
}

@article{fitzhugh_1955,
  title = {Mathematical Models of Threshold Phenomena in the Nerve Membrane},
  author = {FitzHugh, Richard},
  year = {1955},
  month = dec,
  journal = {The Bulletin of Mathematical Biophysics},
  volume = {17},
  number = {4},
  pages = {257--278},
  issn = {0007-4985, 1522-9602},
  doi = {10.1007/BF02477753},
  langid = {english},
  annotation = {00460},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QGIK3J65\\FitzHugh - 1955 - Mathematical models of threshold phenomena in the .pdf}
}

@article{flandin_2007,
  title = {Bayesian {{fMRI}} Data Analysis with Sparse Spatial Basis Function Priors},
  author = {Flandin, Guillaume and Penny, William D.},
  year = {2007},
  month = feb,
  journal = {NeuroImage},
  volume = {34},
  number = {3},
  pages = {1108--1125},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2006.10.005},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8BQVFPSP\\Flandin_Penny_2007_NeuroImage_Bayesian fMRI data analysis with sparse spatial basis function priors.pdf}
}

@incollection{flandin_2015,
  title = {Topological {{Inference}}},
  booktitle = {Brain {{Mapping}}},
  author = {Flandin, G. and Friston, K.J.},
  year = {2015},
  pages = {495--500},
  publisher = {{Elsevier}},
  doi = {10.1016/B978-0-12-397025-1.00322-5},
  isbn = {978-0-12-397316-0},
  langid = {english},
  annotation = {00004},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LAPWNS5F\\Flandin_Friston_2015_Brain Mapping_Topological Inference.pdf}
}

@article{flandin_2017,
  ids = {flandin_analysis_2019},
  title = {Analysis of Family-Wise Error Rates in Statistical Parametric Mapping Using Random Field Theory: {{Family-Wise Error Rate}} in {{Statistical Parametric Mapping}}},
  shorttitle = {Analysis of Family-Wise Error Rates in Statistical Parametric Mapping Using Random Field Theory},
  author = {Flandin, Guillaume and Friston, Karl J.},
  year = {2017},
  month = nov,
  journal = {Human Brain Mapping},
  issn = {10659471},
  doi = {10.1002/hbm.23839},
  abstract = {This technical report revisits the analysis of family-wise error rates in statistical parametric mapping \textendash{} using random field theory \textendash{} reported in (Eklund et al., 2015). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions \textendash{} and random field theory \textendash{} in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al., 2015) for parametric procedures.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\36N7RJFN\\Flandin and Friston - 2017 - Analysis of family-wise error rates in statistical.pdf;C\:\\Users\\dirko\\Zotero\\storage\\9PQBMY78\\Flandin and Friston - 2017 - Analysis of family-wise error rates in statistical.pdf;C\:\\Users\\dirko\\Zotero\\storage\\X4Y854FB\\Flandin und Friston - 2019 - Analysis of family‐wise error rates in statistical.pdf}
}

@book{flury_1997,
  ids = {flury_first_1997a},
  title = {A First Course in Multivariate Statistics},
  author = {Flury, Bernhard and Flury, Bernard},
  year = {1997},
  series = {Springer Texts in Statistics},
  publisher = {{Springer}},
  address = {{New York Heidelberg}},
  isbn = {978-1-4419-3113-9 978-0-387-98206-9},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\G4GDQHAY\\Flury und Flury - 1997 - A first course in multivariate statistics.pdf;C\:\\Users\\dirko\\Zotero\\storage\\T9CI8HEF\\Flury und Flury - 1997 - A first course in multivariate statistics.pdf}
}

@article{forss_1994,
  title = {Activation of the Human Posterior Parietal Cortex by Median Nerve Stimulation},
  author = {Forss, N. and Hari, R. and Salmelin, R. and Ahonen, A. and H{\dbend}m{\dbend}l{\dbend}inen, M. and Kajola, M. and Knuutila, J. and Simola, J.},
  year = {1994},
  month = may,
  journal = {Experimental Brain Research},
  volume = {99},
  number = {2},
  issn = {0014-4819, 1432-1106},
  doi = {10.1007/BF00239597},
  abstract = {We recorded somatosensory evoked magnetic fields from ten healthy, right-handed subjects with a 122-channel whole-scalp SQUID magnetometer. The stimuli, exceeding the motor threshold, were delivered alternately to the left and right median nerves at the wrists, with interstimulus intervals of 1, 3, and 5 s. The first responses, peaking around 20 and 35 ms, were explained by activation of the contralateral primary somatosensory cortex (SI) hand area. All subjects showed additional deflections which peaked after 85 ms; the source locations agreed with the sites of the secondary somatosensory cortices (SII) in both hemispheres. The SII responses were typically stronger in the left than the right hemisphere. All subjects had an additional source, not previously reported in human evoked response data, in the contralateral parietal cortex. This source was posterior and medial to the SI hand area, and evidently in the wall of the postcentral sulcus. It was most active at 70-110 ms.},
  langid = {english},
  annotation = {00000},
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}

@article{forstmann_2016,
  title = {Sequential {{Sampling Models}} in {{Cognitive Neuroscience}}: {{Advantages}}, {{Applications}}, and {{Extensions}}},
  shorttitle = {Sequential {{Sampling Models}} in {{Cognitive Neuroscience}}},
  author = {Forstmann, B.U. and Ratcliff, R. and Wagenmakers, E.-J.},
  year = {2016},
  month = jan,
  journal = {Annual Review of Psychology},
  volume = {67},
  number = {1},
  pages = {641--666},
  issn = {0066-4308, 1545-2085},
  doi = {10.1146/annurev-psych-122414-033645},
  langid = {english},
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}

@article{foulley_1993,
  title = {A Simple Argument Showing How to Derive Restricted Maximum Likelihood},
  author = {Foulley, JL},
  year = {1993},
  annotation = {00018},
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}

@article{fox_,
  title = {Bounded {{Planning}} in {{Passive POMDPs}}},
  author = {Fox, Roy and Tishby, Naftali},
  pages = {8},
  abstract = {In Passive POMDPs actions do not affect the world state, but still incur costs. When the agent is bounded by information-processing constraints, it can only keep an approximation of the belief. We present a variational principle for the problem of maintaining the information which is most useful for minimizing the cost, and introduce an efficient and simple algorithm for finding an optimum.},
  annotation = {00000},
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}

@article{fox_2007,
  title = {Spontaneous Fluctuations in Brain Activity Observed with Functional Magnetic Resonance Imaging},
  author = {Fox, Michael D. and Raichle, Marcus E.},
  year = {2007},
  month = sep,
  journal = {Nature Reviews Neuroscience},
  volume = {8},
  number = {9},
  pages = {700--711},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/nrn2201},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\W534YYLM\\Fox_Raichle_2007_Nature Reviews Neuroscience_Spontaneous fluctuations in brain activity observed with functional magnetic.pdf}
}

@article{fox_2012,
  title = {A Tutorial on Variational {{Bayesian}} Inference},
  author = {Fox, Charles W. and Roberts, Stephen J.},
  year = {2012},
  month = aug,
  journal = {Artificial Intelligence Review},
  volume = {38},
  number = {2},
  pages = {85--95},
  issn = {0269-2821, 1573-7462},
  doi = {10.1007/s10462-011-9236-8},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PGYF87W6\\Fox_Roberts_2012_Artificial Intelligence Review_A tutorial on variational Bayesian inference.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TDH5QMHK\\Fox_Roberts_2012_Artificial Intelligence Review_A tutorial on variational Bayesian inference.pdf}
}

@inproceedings{fox_2016,
  title = {Minimum-Information {{LQG}} Control Part {{I}}: {{Memoryless}} Controllers},
  shorttitle = {Minimum-Information {{LQG}} Control Part {{I}}},
  author = {Fox, Roy and Tishby, Naftali},
  year = {2016},
  month = dec,
  pages = {5610--5616},
  publisher = {{IEEE}},
  doi = {10.1109/CDC.2016.7799131},
  abstract = {With the increased demand for power efficiency in feedback-control systems, communication is becoming a limiting factor, raising the need to trade off the external cost that they incur with the capacity of the controller's communication channels. With a proper design of the channels, this translates into a sequential rate-distortion problem, where we minimize the rate of information required for the controller's operation under a constraint on its external cost. Memoryless controllers are of particular interest both for the simplicity and frugality of their implementation and as a basis for studying more complex controllers. In this paper we present the optimality principle for memoryless linear controllers that utilize minimal information rates to achieve a guaranteed external-cost level. We also study the interesting and useful phenomenology of the optimal controller, such as the principled reduction of its order.},
  isbn = {978-1-5090-1837-6},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\SVV8VIXV\\Fox and Tishby - 2016 - Minimum-information LQG control part I Memoryless.pdf}
}

@book{fox_2019,
  title = {An {{R}} Companion to Applied Regression},
  author = {Fox, John and Weisberg, Sanford},
  year = {2019},
  edition = {Third edition},
  publisher = {{SAGE}},
  address = {{Los Angeles}},
  isbn = {978-1-5443-3647-3},
  langid = {english},
  lccn = {QA278.2 .F628 2019},
  keywords = {Data processing,R (Computer program language),Regression analysis},
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}

@article{franco_2004,
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@book{freedman_2009,
  title = {Statistical {{Models}}: {{Theory}} and {{Practice}}.},
  shorttitle = {Statistical {{Models}}},
  author = {Freedman, David A},
  year = {2009},
  publisher = {{Cambridge University Press}},
  address = {{Leiden}},
  abstract = {Explains the basic ideas of association and regression, taking you through the current models that link these ideas to causality.},
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}

@article{frei_2001,
  title = {Localization of {{MDMA-induced}} Brain Activity in Healthy Volunteers Using Low Resolution Brain Electromagnetic Tomography ({{LORETA}})},
  author = {Frei, Edi and Gamma, Alex and {Pascual-Marqui}, Roberto and Lehmann, Dietrich and Hell, Daniel and Vollenweider, Franz X.},
  year = {2001},
  month = nov,
  journal = {Human Brain Mapping},
  volume = {14},
  number = {3},
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  issn = {1065-9471, 1097-0193},
  doi = {10.1002/hbm.1049},
  abstract = {3,4-Methylenedioxymethamphetamine (MDMA; 'Ecstasy') is a psychostimulant drug producing heightened mood and facilitated social communication. In animal studies, MDMA effects are primarily mediated by serotonin (5-HT), but also by dopamine (DA) and possibly noradrenaline (NA). In humans, however, the neurochemical and neurophysiological basis of acute MDMA effects remains unknown. The distribution of active neuronal populations after administration of a single dose of MDMA (1.7 mg/kg) or placebo was studied in 16 healthy, MDMA-na\textasciidieresis\i ve volunteers. Thirty-one-channel scalp EEGs during resting with open and closed eyes was analyzed in the different EEG frequency bands. Scalp maps of power showed significant, global differences between MDMA and placebo in both eye conditions and all frequency bands. Low resolution brain electromagnetic tomography (LORETA) was used to compute 3D, functional images of electric neuronal activity from the scalp EEG data. MDMA produced a widespread decrease of slow and medium frequency activity and an increase of fast frequency activity in the anterior temporal and posterior orbital cortex, concomitant with a marked enhancement of mood, emotional arousal and increased extraversion. This activation of frontotemporal areas indicates that the observed enhancement of mood and possibly the increased extroversion rely on modulation of limbic orbitofrontal and anterotemporal structures known to be involved in emotional processes. Comparison of the MDMAspecific EEG pattern with that of various 5-HT, DA, and NA agonists indicates that serotonin, noradrenaline, and, to a lesser degree, dopamine, contribute to the effects of MDMA on EEG, and possibly also on mood and behavior. Hum. Brain Mapping 14:152\textendash 165, 2001. \textcopyright{} 2001 Wiley-Liss, Inc.},
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}

@article{freyer_2012,
  title = {A {{Canonical Model}} of {{Multistability}} and {{Scale-Invariance}} in {{Biological Systems}}},
  author = {Freyer, Frank and Roberts, James A. and Ritter, Petra and Breakspear, Michael},
  editor = {Behrens, Tim},
  year = {2012},
  month = aug,
  journal = {PLoS Computational Biology},
  volume = {8},
  number = {8},
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  doi = {10.1371/journal.pcbi.1002634},
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}

@book{friedman_1970,
  title = {Foundations of {{Modern Analysis}}},
  author = {Friedman, Avner},
  year = {1970},
  publisher = {{Dover Publications}},
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}

@article{frison_1992,
  ids = {frison_repeated_1992-1},
  title = {Repeated Measures in Clinical Trials: {{Analysis}} Using Mean Summary Statistics and Its Implications for Design},
  shorttitle = {Repeated Measures in Clinical Trials},
  author = {Frison, Lars and Pocock, Stuart J.},
  year = {1992},
  journal = {Statistics in Medicine},
  volume = {11},
  number = {13},
  pages = {1685--1704},
  issn = {02776715, 10970258},
  doi = {10.1002/sim.4780111304},
  abstract = {This paper explores the use of simple summary statistics for analysing repeated measurements in randomized clinical trials with two treatments. Quite often the data for each patient may be effectively summarized by a pre-treatment mean and a post-treatment mean. Analysis of covariance is the method of choice and its superiority over analysis of post-treatment means or analysis of mean changes is quantified, as regards both reduced variance and avoidance of bias, using a simple model for the covariance structure between time points. Quantitative consideration is also given to practical issues in the design of repeated measures studies: the merits of having more than one pre-treatment measurement are demonstrated, and methods for determining sample sizes in repeated measures designs are provided. Several examples from clinical trials are presented, and broad practical recommendations are made. The examples support the value of the compound symmetry assumption as a realistic simplification in quantitative planning of repeated measures trials. The analysis using summary statistics makes no such assumption. However, allowance in design for alternative non-equal correlation structures can and should be made when necessary.},
  langid = {english},
  annotation = {00623},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SWX85PPV\\Frison und Pocock - 1992 - Repeated measures in clinical trials Analysis usi.pdf;C\:\\Users\\dirko\\Zotero\\storage\\U67CATC6\\Frison und Pocock - 1992 - Repeated measures in clinical trials Analysis usi.pdf}
}

@book{fristedt_1998,
  ids = {fristedt_modern_1997},
  title = {A {{Modern Approach}} to {{Probability Theory}}},
  author = {Fristedt, Bert E and Gray, Lawrence F and {Birkh\"auser Publishing Ltd}},
  year = {1998},
  doi = {10.1007/978-1-4899-2837-5},
  isbn = {978-1-4899-2839-9},
  langid = {english},
  annotation = {00495  OCLC: 1081350846},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5TKPLF7L\\Fristedt et al. - 1998 - A Modern Approach to Probability Theory.pdf}
}

@article{friston_,
  title = {The Free Energy Principle Made Simpler but Not Too Simple},
  author = {Friston, Karl and Costa, Lancelot Da and Sajid, Noor and Heins, Conor and Ueltzh{\"o}ffer, Kai and Pavliotis, Grigorios A and Parr, Thomas},
  pages = {44},
  abstract = {This paper provides a concise description of the free energy principle, starting from a formulation of random dynamical systems in terms of a Langevin equation and ending with a Bayesian mechanics that can be read as a physics of sentience. It rehearses the key steps using standard results from statistical physics. These steps entail (i) establishing a particular partition of states based upon conditional independencies that inherit from sparsely coupled dynamics, (ii) unpacking the implications of this partition in terms of Bayesian inference and (iii) describing the paths of particular states with a variational principle of least action. Teleologically, the free energy principle offers a normative account of self-organisation in terms of optimal Bayesian design and decision-making, in the sense of maximising marginal likelihood or Bayesian model evidence. In summary, starting from a description of the world in terms of random dynamical systems, we end up with a description of self-organisation as sentient behaviour that can be interpreted as self-evidencing; namely, self-assembly, autopoiesis or active inference.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RJ48NTJU\\Friston et al. - The free energy principle made simpler but not too.pdf}
}

@article{friston_1991,
  title = {Comparing Functional ({{PET}}) Images: The Assessment of Significant Change},
  shorttitle = {Comparing Functional ({{PET}}) Images},
  author = {Friston, Karl J. and Frith, C. D. and Liddle, P. F. and Frackowiak, R. S. J.},
  year = {1991},
  journal = {Journal of Cerebral Blood Flow \& Metabolism},
  volume = {11},
  number = {4},
  pages = {690--699},
  annotation = {01811},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JD37DF6H\\Friston_JCBF_91.pdf}
}

@article{friston_1992,
  title = {Entropy and Cortical Activity: Information Theory and {{PET}} Findings},
  shorttitle = {Entropy and Cortical Activity},
  author = {Friston, K. J. and Frith, C. D. and Passingham, R. E. and Dolan, R. J. and Liddle, P. F. and Frackowiak, R. S. J.},
  year = {1992},
  journal = {Cerebral Cortex},
  volume = {2},
  number = {3},
  pages = {259--267},
  annotation = {00019},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BPGIA3H3\\Friston_CC_92.pdf}
}

@article{friston_1994,
  title = {Assessing the Significance of Focal Activations Using Their Spatial Extent},
  author = {Friston, K. J. and Worsley, K. J. and Frackowiak, R. S. and Mazziotta, J. C. and Evans, A. C.},
  year = {1994},
  journal = {Human Brain Mapping},
  volume = {1},
  number = {3},
  pages = {210--220},
  issn = {1065-9471},
  doi = {10.1002/hbm.460010306},
  abstract = {Current approaches to detecting significantly activated regions of cerebral tissue use statistical parametric maps, which are thresholded to render the probability of one or more activated regions of one voxel, or larger, suitably small (e. g., 0.05). We present an approximate analysis giving the probability that one or more activated regions of a specified volume, or larger, could have occurred by chance. These results mean that detecting significant activations no longer depends on a fixed (and high) threshold, but can be effected at any (lower) threshold, in terms of the spatial extent of the activated region. The substantial improvement in sensitivity that ensues is illustrated using a power analysis and a simulated phantom activation study. \textcopyright{} 1994 Wiley-Liss, Inc.},
  langid = {english},
  pmid = {24578041},
  keywords = {activation,excursion set,functional imaging,gaussian fields,statistical parametric mapping,thresholds},
  annotation = {01787},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8YVSBJSH\\Friston K. J. et al_2004_Human Brain Mapping_Assessing the significance of focal activations using their spatial extent.pdf;C\:\\Users\\dirko\\Zotero\\storage\\7ZWZWPZE\\hbm.html}
}

@article{friston_1994a,
  title = {Statistical Parametric Maps in Functional Imaging: A General Linear Approach},
  shorttitle = {Statistical Parametric Maps in Functional Imaging},
  author = {Friston, Karl J. and Holmes, Andrew P. and Worsley, Keith J. and Poline, J.-P. and Frith, Chris D. and Frackowiak, Richard SJ},
  year = {1994},
  journal = {Human brain mapping},
  volume = {2},
  number = {4},
  pages = {189--210},
  annotation = {09017},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\54ERS5LZ\\Friston_HBM_95.pdf}
}

@article{friston_1996,
  title = {Detecting Activations in {{PET}} and {{fMRI}}: Levels of Inference and Power},
  shorttitle = {Detecting Activations in {{PET}} and {{fMRI}}},
  author = {Friston, Karl J. and Holmes, Andrew and Poline, Jean-Baptiste and Price, Cathy J. and Frith, Christopher D.},
  year = {1996},
  journal = {Neuroimage},
  volume = {4},
  number = {3},
  pages = {223--235},
  annotation = {01041},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BX7BUZFB\\Friston et al_1996_Neuroimage_Detecting activations in PET and fMRI.pdf}
}

@article{friston_1996a,
  title = {The Trouble with Cognitive Subtraction},
  author = {Friston, Karl J. and Price, C. J. and Fletcher, Paul and Moore, C. and Frackowiak, R. S. J. and Dolan, R. J.},
  year = {1996},
  journal = {Neuroimage},
  volume = {4},
  number = {2},
  pages = {97--104},
  annotation = {00565},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SCSXSCED\\Friston et al_1996_Neuroimage_The trouble with cognitive subtraction.pdf}
}

@article{friston_1997,
  title = {Psychophysiological and {{Modulatory Interactions}} in {{Neuroimaging}}},
  author = {Friston, K.J and Buechel, C and Fink, G.R and Morris, J and Rolls, E and Dolan, R.J},
  year = {1997},
  month = oct,
  journal = {NeuroImage},
  volume = {6},
  number = {3},
  pages = {218--229},
  issn = {10538119},
  doi = {10.1006/nimg.1997.0291},
  langid = {english},
  annotation = {02543},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SQKP2TIZ\\Friston et al. - 1997 - Psychophysiological and Modulatory Interactions in.pdf}
}

@article{friston_1998,
  title = {Event-Related {{fMRI}}: Characterizing Differential Responses},
  shorttitle = {Event-Related {{fMRI}}},
  author = {Friston, Karl J. and Fletcher, P. and Josephs, Oliver and Holmes, ANDREW and Rugg, M. D. and Turner, Robert},
  year = {1998},
  journal = {Neuroimage},
  volume = {7},
  number = {1},
  pages = {30--40},
  annotation = {01842},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\59IX6JW8\\Friston_NI_98.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PGZDYMQL\\FristonK_NI_98.pdf}
}

@article{friston_1999,
  title = {Stochastic Designs in Event-Related {{fMRI}}},
  author = {Friston, Karl J. and Zarahn, EORNA and Josephs, O. and Henson, R. N. A. and Dale, Anders M.},
  year = {1999},
  journal = {Neuroimage},
  volume = {10},
  number = {5},
  pages = {607--619},
  annotation = {00594},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\W4FEZ4TP\\Friston_Zarahn_Josephs_Henson_Dale_NI_99.pdf}
}

@article{friston_2002,
  title = {Bayesian {{Estimation}} of {{Dynamical Systems}}: {{An Application}} to {{fMRI}}},
  shorttitle = {Bayesian {{Estimation}} of {{Dynamical Systems}}},
  author = {Friston, K.J.},
  year = {2002},
  month = jun,
  journal = {NeuroImage},
  volume = {16},
  number = {2},
  pages = {513--530},
  issn = {10538119},
  doi = {10.1006/nimg.2001.1044},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UYUZI8AP\\Friston_2002_NeuroImage_Bayesian Estimation of Dynamical Systems.pdf}
}

@article{friston_2002a,
  title = {Classical and {{Bayesian Inference}} in {{Neuroimaging}}: {{Applications}}},
  shorttitle = {Classical and {{Bayesian Inference}} in {{Neuroimaging}}},
  author = {Friston, K.J. and Glaser, D.E. and Henson, R.N.A. and Kiebel, S. and Phillips, C. and Ashburner, J.},
  year = {2002},
  month = jun,
  journal = {NeuroImage},
  volume = {16},
  number = {2},
  pages = {484--512},
  issn = {10538119},
  doi = {10.1006/nimg.2002.1091},
  langid = {english},
  annotation = {00657},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Q9BCCD2Y\\Friston et al. - 2002 - Classical and Bayesian Inference in Neuroimaging .pdf}
}

@article{friston_2002b,
  title = {Classical and {{Bayesian Inference}} in {{Neuroimaging}}: {{Theory}}},
  shorttitle = {Classical and {{Bayesian Inference}} in {{Neuroimaging}}},
  author = {Friston, K.J. and Penny, W. and Phillips, C. and Kiebel, S. and Hinton, G. and Ashburner, J.},
  year = {2002},
  month = jun,
  journal = {NeuroImage},
  volume = {16},
  number = {2},
  pages = {465--483},
  issn = {10538119},
  doi = {10.1006/nimg.2002.1090},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DHCRFXLQ\\Friston et al_2002_NeuroImage_Classical and Bayesian Inference in Neuroimaging.pdf}
}

@article{friston_2003,
  title = {Dynamic Causal Modelling},
  author = {Friston, K.J. and Harrison, L. and Penny, W.},
  year = {2003},
  month = aug,
  journal = {NeuroImage},
  volume = {19},
  number = {4},
  pages = {1273--1302},
  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00202-7},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5CY2XJ3M\\Friston et al_2003_NeuroImage_Dynamic causal modelling.pdf}
}

@article{friston_2003a,
  title = {Learning and Inference in the Brain},
  author = {Friston, Karl},
  year = {2003},
  month = nov,
  journal = {Neural Networks},
  volume = {16},
  number = {9},
  pages = {1325--1352},
  issn = {08936080},
  doi = {10.1016/j.neunet.2003.06.005},
  langid = {english},
  annotation = {00395},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\22GPXX6S\\Friston_2003_Neural Networks_Learning and inference in the brain.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UYRDGGFA\\Friston_2003_Neural Networks_Learning and inference in the brain.pdf}
}

@article{friston_2003b,
  title = {Posterior Probability Maps and {{SPMs}}},
  author = {Friston, K.J. and Penny, W.},
  year = {2003},
  month = jul,
  journal = {NeuroImage},
  volume = {19},
  number = {3},
  pages = {1240--1249},
  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00144-7},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\G3FKZ4P8\\Friston_Penny_2003_NeuroImage_Posterior probability maps and SPMs.pdf}
}

@article{friston_2005,
  title = {A Theory of Cortical Responses},
  author = {Friston, K.},
  year = {2005},
  month = apr,
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {360},
  number = {1456},
  pages = {815--836},
  issn = {0962-8436, 1471-2970},
  doi = {10.1098/rstb.2005.1622},
  langid = {english},
  annotation = {01813},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VR7KVXCP\\Friston_2005_Philosophical Transactions of the Royal Society B Biological Sciences_A theory of cortical responses.pdf;C\:\\Users\\dirko\\Zotero\\storage\\WY7ITZEP\\Friston_2005_Philosophical Transactions of the Royal Society B Biological Sciences_A theory of cortical responses.pdf}
}

@article{friston_2005a,
  title = {Conjunction Revisited},
  author = {Friston, Karl J. and Penny, William D. and Glaser, Daniel E.},
  year = {2005},
  month = apr,
  journal = {NeuroImage},
  volume = {25},
  number = {3},
  pages = {661--667},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2005.01.013},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\IUR8SPJW\\Friston et al_2005_NeuroImage_Conjunction revisited.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UKI5CYEV\\Friston et al_2005_NeuroImage_Conjunction revisited.pdf}
}

@article{friston_2006,
  title = {A Critique of Functional Localisers},
  author = {Friston, K.J. and Rotshtein, P. and Geng, J.J. and Sterzer, P. and Henson, R.N.},
  year = {2006},
  month = may,
  journal = {NeuroImage},
  volume = {30},
  number = {4},
  pages = {1077--1087},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2005.08.012},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3NXNGEWT\\Friston et al_2006_NeuroImage_A critique of functional localisers.pdf;C\:\\Users\\dirko\\Zotero\\storage\\ULJ8PMFK\\Friston et al. - 2006 - A critique of functional localisers.pdf}
}

@article{friston_2007,
  title = {Free-Energy and the Brain},
  author = {Friston, Karl J. and Stephan, Klaas E.},
  year = {2007},
  month = nov,
  journal = {Synthese},
  volume = {159},
  number = {3},
  pages = {417--458},
  issn = {0039-7857, 1573-0964},
  doi = {10.1007/s11229-007-9237-y},
  langid = {english},
  annotation = {00319},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7VS8ZJED\\Friston_Stephan_2007_Synthese_Free-energy and the brain.pdf;C\:\\Users\\dirko\\Zotero\\storage\\IL7EF8H9\\Friston_Stephan_2007_Synthese_Free-energy and the brain.pdf}
}

@book{friston_2007a,
  title = {Statistical Parametric Mapping: The Analysis of Funtional Brain Images},
  shorttitle = {Statistical Parametric Mapping},
  editor = {Friston, K. J.},
  year = {2007},
  edition = {1st ed},
  publisher = {{Elsevier/Academic Press}},
  address = {{Amsterdam ; Boston}},
  isbn = {978-0-12-372560-8},
  lccn = {RC386.6.B7 S73 2007},
  keywords = {Brain,Brain mapping,Imaging Statistical methods,Statistical methods},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QND9RR3S\\Friston_2007_Statistical parametric mapping.pdf}
}

@article{friston_2007b,
  title = {Variational Free Energy and the {{Laplace}} Approximation},
  author = {Friston, Karl and Mattout, J{\'e}r{\'e}mie and {Trujillo-Barreto}, Nelson and Ashburner, John and Penny, Will},
  year = {2007},
  month = jan,
  journal = {NeuroImage},
  volume = {34},
  number = {1},
  pages = {220--234},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2006.08.035},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8EBTQSLB\\Friston et al_2007_NeuroImage_Variational free energy and the Laplace approximation.pdf;C\:\\Users\\dirko\\Zotero\\storage\\CSIQF3V6\\Friston et al_2007_NeuroImage_Variational free energy and the Laplace approximation.pdf}
}

@article{friston_2008,
  title = {Bayesian Decoding of Brain Images},
  author = {Friston, Karl and Chu, Carlton and {Mour{\~a}o-Miranda}, Janaina and Hulme, Oliver and Rees, Geraint and Penny, Will and Ashburner, John},
  year = {2008},
  month = jan,
  journal = {NeuroImage},
  volume = {39},
  number = {1},
  pages = {181--205},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.08.013},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3SBKUP95\\Friston et al_2008_NeuroImage_Bayesian decoding of brain images.pdf}
}

@article{friston_2008a,
  title = {{{DEM}}: {{A}} Variational Treatment of Dynamic Systems},
  shorttitle = {{{DEM}}},
  author = {Friston, K.J. and {Trujillo-Barreto}, N. and Daunizeau, J.},
  year = {2008},
  month = jul,
  journal = {NeuroImage},
  volume = {41},
  number = {3},
  pages = {849--885},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.02.054},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MU4Q23BI\\Friston et al_2008_NeuroImage_DEM.pdf}
}

@article{friston_2008b,
  title = {Hierarchical {{Models}} in the {{Brain}}},
  author = {Friston, Karl},
  editor = {Sporns, Olaf},
  year = {2008},
  month = nov,
  journal = {PLoS Computational Biology},
  volume = {4},
  number = {11},
  pages = {e1000211},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1000211},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5GTIF2CM\\Friston_2008_PLoS Computational Biology_Hierarchical Models in the Brain.pdf}
}

@article{friston_2008c,
  title = {Variational Filtering},
  author = {Friston, K.J.},
  year = {2008},
  month = jul,
  journal = {NeuroImage},
  volume = {41},
  number = {3},
  pages = {747--766},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.03.017},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UNU7BSF4\\Friston_2008_NeuroImage_Variational filtering.pdf}
}

@article{friston_2009,
  title = {Attractors in Song},
  author = {Friston, Karl and Kiebel, Stefan},
  year = {2009},
  journal = {New Mathematics and Natural Computation},
  volume = {5},
  number = {01},
  pages = {83--114},
  annotation = {00008},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Y6PULIWU\\Friston_Kiebel_NMNC_09.pdf}
}

@article{friston_2009a,
  title = {Modalities, {{Modes}}, and {{Models}} in {{Functional Neuroimaging}}},
  author = {Friston, K. J.},
  year = {2009},
  month = oct,
  journal = {Science},
  volume = {326},
  number = {5951},
  pages = {399--403},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1174521},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\928VFVST\\Friston_2009_Science_Modalities, Modes, and Models in Functional Neuroimaging.pdf}
}

@article{friston_2009b,
  title = {Reinforcement Learning or Active Inference?},
  author = {Friston, Karl J. and Daunizeau, Jean and Kiebel, Stefan J.},
  year = {2009},
  journal = {PLoS ONE},
  volume = {4},
  number = {7},
  issn = {19326203},
  doi = {10.1371/journal.pone.0006421},
  abstract = {This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.},
  pmid = {19641614},
  annotation = {00000}
}

@article{friston_2009c,
  title = {The Free-Energy Principle: A Rough Guide to the Brain?},
  shorttitle = {The Free-Energy Principle},
  author = {Friston, Karl},
  year = {2009},
  month = jul,
  journal = {Trends in Cognitive Sciences},
  volume = {13},
  number = {7},
  pages = {293--301},
  issn = {13646613},
  doi = {10.1016/j.tics.2009.04.005},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\92CIBQM4\\Friston_2009_Trends in Cognitive Sciences_The free-energy principle.pdf}
}

@article{friston_2010,
  title = {Computational and Dynamic Models in Neuroimaging},
  author = {Friston, Karl J. and Dolan, Raymond J.},
  year = {2010},
  month = sep,
  journal = {NeuroImage},
  volume = {52},
  number = {3},
  pages = {752--765},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.12.068},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KJU9BKWQ\\Friston_Dolan_2010_NeuroImage_Computational and dynamic models in neuroimaging.pdf}
}

@article{friston_2010a,
  title = {Generalised {{Filtering}}},
  author = {Friston, Karl and Stephan, Klaas and Li, Baojuan and Daunizeau, Jean},
  year = {2010},
  journal = {Mathematical Problems in Engineering},
  volume = {2010},
  pages = {1--34},
  issn = {1024-123X, 1563-5147},
  doi = {10.1155/2010/621670},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\IDTMZZLM\\Friston et al_2010_Mathematical Problems in Engineering_Generalised Filtering.pdf;C\:\\Users\\dirko\\Zotero\\storage\\LU9CJECG\\Friston et al_2010_Mathematical Problems in Engineering_Generalised Filtering.pdf}
}

@article{friston_2010b,
  title = {The Free-Energy Principle: A Unified Brain Theory?},
  shorttitle = {The Free-Energy Principle},
  author = {Friston, Karl},
  year = {2010},
  month = feb,
  journal = {Nature Reviews Neuroscience},
  volume = {11},
  number = {2},
  pages = {127--138},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/nrn2787},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\66D3P7WX\\Friston_2010_Nature Reviews Neuroscience_The free-energy principle.pdf}
}

@article{friston_2011,
  title = {Embodied Inference: {{Or I}} Think Therefore {{I}} Am, If {{I}} Am What {{I}} Think},
  shorttitle = {Embodied Inference},
  author = {Friston, Karl},
  year = {2011},
  journal = {The implications of embodiment (Cognition and Communication)},
  pages = {89--125},
  annotation = {00081},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BQARRUXM\\Friston_BookChapter_11.pdf}
}

@article{friston_2011a,
  title = {Modules and Brain Mapping},
  author = {Friston, Karl J. and Price, Cathy J.},
  year = {2011},
  month = jun,
  journal = {Cognitive Neuropsychology},
  volume = {28},
  number = {3-4},
  pages = {241--250},
  issn = {0264-3294, 1464-0627},
  doi = {10.1080/02643294.2011.558835},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ITQHCE92\\Friston_Price_2011_Cognitive Neuropsychology_Modules and brain mapping.pdf}
}

@article{friston_2011b,
  title = {Network Discovery with {{DCM}}},
  author = {Friston, Karl J. and Li, Baojuan and Daunizeau, Jean and Stephan, Klaas E.},
  year = {2011},
  month = jun,
  journal = {NeuroImage},
  volume = {56},
  number = {3},
  pages = {1202--1221},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2010.12.039},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3447CWBG\\Friston et al_2011_NeuroImage_Network discovery with DCM.pdf}
}

@article{friston_2012,
  title = {{{DCM}} for Complex-Valued Data: {{Cross-spectra}}, Coherence and Phase-Delays},
  shorttitle = {{{DCM}} for Complex-Valued Data},
  author = {Friston, K.J. and Bastos, A. and Litvak, V. and Stephan, K.E. and Fries, P. and Moran, R.J.},
  year = {2012},
  month = jan,
  journal = {NeuroImage},
  volume = {59},
  number = {1},
  pages = {439--455},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2011.07.048},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KHQ7G7NT\\Friston et al_2012_NeuroImage_DCM for complex-valued data.pdf}
}

@article{friston_2012a,
  title = {Dopamine, {{Affordance}} and {{Active Inference}}},
  author = {Friston, Karl J. and Shiner, Tamara and FitzGerald, Thomas and Galea, Joseph M. and Adams, Rick and Brown, Harriet and Dolan, Raymond J. and Moran, Rosalyn and Stephan, Klaas Enno and Bestmann, Sven},
  editor = {Sporns, Olaf},
  year = {2012},
  month = jan,
  journal = {PLoS Computational Biology},
  volume = {8},
  number = {1},
  pages = {e1002327},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1002327},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5GRQ3IRL\\Friston et al_2012_PLoS Computational Biology_Dopamine, Affordance and Active Inference.pdf}
}

@article{friston_2012b,
  title = {Free-{{Energy Minimization}} and the {{Dark-Room Problem}}},
  author = {Friston, Karl and Thornton, Christopher and Clark, Andy},
  year = {2012},
  journal = {Frontiers in Psychology},
  volume = {3},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2012.00130},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AGHA8Z77\\Friston et al_2012_Frontiers in Psychology_Free-Energy Minimization and the Dark-Room Problem.pdf}
}

@article{friston_2012c,
  title = {Perceptions as {{Hypotheses}}: {{Saccades}} as {{Experiments}}},
  shorttitle = {Perceptions as {{Hypotheses}}},
  author = {Friston, Karl and Adams, Rick A. and Perrinet, Laurent and Breakspear, Michael},
  year = {2012},
  journal = {Frontiers in Psychology},
  volume = {3},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2012.00151},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7R5F3ETH\\Friston et al_2012_Frontiers in Psychology_Perceptions as Hypotheses.pdf}
}

@article{friston_2012d,
  title = {Ten Ironic Rules for Non-Statistical Reviewers},
  author = {Friston, Karl},
  year = {2012},
  month = jul,
  journal = {NeuroImage},
  volume = {61},
  number = {4},
  pages = {1300--1310},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.04.018},
  abstract = {As an expert reviewer, it is sometimes necessary to ensure a paper is rejected. This can sometimes be achieved by highlighting improper statistical practice. This technical note provides guidance on how to critique the statistical analysis of neuroimaging studies to maximise the chance that the paper will be declined. We will review a series of critiques that can be applied universally to any neuroimaging paper and consider responses to potential rebuttals that reviewers might encounter from authors or editors.},
  langid = {english},
  annotation = {00247},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\36TXUX44\\Friston - 2012 - Ten ironic rules for non-statistical reviewers.pdf}
}

@article{friston_2012e,
  title = {What Is Value\textemdash Accumulated Reward or Evidence?},
  author = {Friston, Karl and Adams, Rick and Montague, Read},
  year = {2012},
  journal = {Frontiers in Neurorobotics},
  volume = {6},
  issn = {1662-5218},
  doi = {10.3389/fnbot.2012.00011},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7KF3LBGK\\Friston et al_2012_Frontiers in Neurorobotics_What is value—accumulated reward or evidence.pdf}
}

@article{friston_2013,
  title = {Model Selection and Gobbledygook: {{Response}} to {{Lohmann}} et Al.},
  shorttitle = {Model Selection and Gobbledygook},
  author = {Friston, Karl and Daunizeau, Jean and Stephan, Klaas Enno},
  year = {2013},
  journal = {Neuroimage},
  volume = {75},
  pages = {275--278},
  annotation = {00026},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XINP4T42\\Friston_Vs_Lohmann_NI_11.pdf}
}

@article{friston_2013a,
  title = {Sample Size and the Fallacies of Classical Inference},
  author = {Friston, Karl},
  year = {2013},
  month = nov,
  journal = {NeuroImage},
  volume = {81},
  pages = {503--504},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2013.02.057},
  langid = {english},
  annotation = {00021},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\79I4PN2Z\\Friston_2013_NeuroImage_Sample size and the fallacies of classical inference.pdf;C\:\\Users\\dirko\\Zotero\\storage\\WV2YLLVQ\\Friston_2013_NeuroImage_Sample size and the fallacies of classical inference.pdf}
}

@article{friston_2013b,
  title = {The Anatomy of Choice: Active Inference and Agency},
  shorttitle = {The Anatomy of Choice},
  author = {Friston, Karl and Schwartenbeck, Philipp and FitzGerald, Thomas and Moutoussis, Michael and Behrens, Timothy and Dolan, Raymond J.},
  year = {2013},
  journal = {Frontiers in Human Neuroscience},
  volume = {7},
  issn = {1662-5161},
  doi = {10.3389/fnhum.2013.00598},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AHWBGUEG\\Friston et al_2013_Frontiers in Human Neuroscience_The anatomy of choice.pdf}
}

@article{friston_2014,
  title = {Computational Psychiatry: The Brain as a Phantastic Organ},
  shorttitle = {Computational Psychiatry},
  author = {Friston, Karl J and Stephan, Klaas Enno and Montague, Read and Dolan, Raymond J},
  year = {2014},
  month = jul,
  journal = {The Lancet Psychiatry},
  volume = {1},
  number = {2},
  pages = {148--158},
  issn = {22150366},
  doi = {10.1016/S2215-0366(14)70275-5},
  langid = {english},
  annotation = {00256},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JDYSWKQ5\\Friston et al. - 2014 - Computational psychiatry the brain as a phantasti.pdf}
}

@article{friston_2015,
  title = {Active Inference and Epistemic Value},
  author = {Friston, Karl and Rigoli, Francesco and Ognibene, Dimitri and Mathys, Christoph and Fitzgerald, Thomas and Pezzulo, Giovanni},
  year = {2015},
  journal = {Cognitive Neuroscience},
  volume = {6},
  number = {4},
  pages = {187--224},
  issn = {17588936},
  doi = {10.1080/17588928.2015.1020053},
  abstract = {We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes- optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.},
  pmid = {25689102},
  keywords = {Active inference,Agency,Bayesian inference,Bayesian surprise,Bounded rationality,Epistemic value,Exploitation,Exploration,Free energy,Information gain,Utility theory},
  annotation = {00000}
}

@article{friston_2016,
  title = {Bayesian Model Reduction and Empirical {{Bayes}} for Group ({{DCM}}) Studies},
  author = {Friston, Karl J. and Litvak, Vladimir and Oswal, Ashwini and Razi, Adeel and Stephan, Klaas E. and {van Wijk}, Bernadette C.M. and Ziegler, Gabriel and Zeidman, Peter},
  year = {2016},
  month = mar,
  journal = {NeuroImage},
  volume = {128},
  pages = {413--431},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2015.11.015},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TTPW6JA6\\Friston et al_2016_NeuroImage_Bayesian model reduction and empirical Bayes for group (DCM) studies.pdf}
}

@article{friston_2017,
  title = {Active {{Inference}}: {{A Process Theory}}},
  shorttitle = {Active {{Inference}}},
  author = {Friston, Karl and FitzGerald, Thomas and Rigoli, Francesco and Schwartenbeck, Philipp and Pezzulo, Giovanni},
  year = {2017},
  month = jan,
  journal = {Neural Computation},
  volume = {29},
  number = {1},
  pages = {1--49},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/NECO_a_00912},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6DQHR94Q\\Friston et al_2017_Neural Computation_Active Inference.pdf;C\:\\Users\\dirko\\Zotero\\storage\\6EDCZVXY\\Friston et al_2017_Neural Computation_Active Inference.pdf}
}

@article{friston_2017a,
  title = {Dynamic Causal Modelling Revisited},
  author = {Friston, K.J. and Preller, Katrin H. and Mathys, Chris and Cagnan, Hayriye and Heinzle, Jakob and Razi, Adeel and Zeidman, Peter},
  year = {2017},
  month = feb,
  journal = {NeuroImage},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2017.02.045},
  abstract = {This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells \textendash{} or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.},
  langid = {english},
  annotation = {00034},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PHD9BZTV\\Friston et al. - 2017 - Dynamic causal modelling revisited.pdf}
}

@article{friston_2018,
  title = {Does Predictive Coding Have a Future?},
  author = {Friston, Karl},
  year = {2018},
  month = aug,
  journal = {Nature Neuroscience},
  volume = {21},
  number = {8},
  pages = {1019--1021},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-018-0200-7},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7LZT8WP2\\Friston - 2018 - Does predictive coding have a future.pdf}
}

@article{friston_2019,
  title = {Variational Representational Similarity Analysis},
  author = {Friston, Karl J. and Diedrichsen, J{\"o}rn and Holmes, Emma and Zeidman, Peter},
  year = {2019},
  month = jun,
  journal = {NeuroImage},
  pages = {S1053811919305610},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2019.06.064},
  abstract = {This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over voxels or channels. On this view, one can use standard variational inference procedures to quantify the contributions of particular patterns to the data, by evaluating second-order parameters or hyperparameters. Crucially, this allows one to use parametric empirical Bayes (PEB) to infer which patterns are consistent among subjects. At the between-subject level, one can then assess the evidence for different (combinations of) hypotheses about condition-specific effects using Bayesian model comparison. Alternatively, one can select a single hypothesis that best explains the pattern of responses using Bayesian model selection. This note rehearses the technical aspects of within and between-subject RSA using a worked example, as implemented in the Statistical Parametric Mapping (SPM) software. En route, we highlight the connection between univariate and multivariate analyses of neuroimaging data and the sorts of analyses that are possible using component modelling and representational similarity analysis.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\39552WVP\\Friston et al. - 2019 - Variational representational similarity analysis.pdf}
}

@article{friston_2020,
  title = {Sentience and the {{Origins}} of {{Consciousness}}: {{From Cartesian Duality}} to {{Markovian Monism}}},
  shorttitle = {Sentience and the {{Origins}} of {{Consciousness}}},
  author = {Friston, Karl J. and Wiese, Wanja and Hobson, J. Allan},
  year = {2020},
  month = apr,
  journal = {Entropy},
  volume = {22},
  number = {5},
  pages = {516},
  issn = {1099-4300},
  doi = {10.3390/e22050516},
  abstract = {This essay addresses Cartesian duality and how its implicit dialectic might be repaired using physics and information theory. Our agenda is to describe a key distinction in the physical sciences that may provide a foundation for the distinction between mind and matter, and between sentient and intentional systems. From this perspective, it becomes tenable to talk about the physics of sentience and `forces' that underwrite our beliefs (in the sense of probability distributions represented by our internal states), which may ground our mental states and consciousness. We will refer to this view as Markovian monism, which entails two claims: (1) fundamentally, there is only one type of thing and only one type of irreducible property (hence monism). (2) All systems possessing a Markov blanket have properties that are relevant for understanding the mind and consciousness: if such systems have mental properties, then they have them partly by virtue of possessing a Markov blanket (hence Markovian). Markovian monism rests upon the information geometry of random dynamic systems. In brief, the information geometry induced in any system\textemdash whose internal states can be distinguished from external states\textemdash must acquire a dual aspect. This dual aspect concerns the (intrinsic) information geometry of the probabilistic evolution of internal states and a separate (extrinsic) information geometry of probabilistic beliefs about external states that are parameterised by internal states. We call these intrinsic (i.e., mechanical, or state-based) and extrinsic (i.e., Markovian, or belief-based) information geometries, respectively. Although these mathematical notions may sound complicated, they are fairly straightforward to handle, and may offer a means through which to frame the origins of consciousness.},
  langid = {english},
  annotation = {00012},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YNB6YT8P\\Friston et al. - 2020 - Sentience and the Origins of Consciousness From C.pdf}
}

@article{friston_2021,
  title = {World Model Learning and Inference},
  author = {Friston, Karl and Moran, Rosalyn J. and Nagai, Yukie and Taniguchi, Tadahiro and Gomi, Hiroaki and Tenenbaum, Josh},
  year = {2021},
  month = dec,
  journal = {Neural Networks},
  volume = {144},
  pages = {573--590},
  issn = {08936080},
  doi = {10.1016/j.neunet.2021.09.011},
  abstract = {Understanding information processing in the brain\textemdash and creating general-purpose artificial intelligence\textemdash are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZTPHKPVN\\Friston et al. - 2021 - World model learning and inference.pdf}
}

@article{frund_2007,
  title = {{{EEG}} Oscillations in the Gamma and Alpha Range Respond Differently to Spatial Frequency},
  author = {Fr{\"u}nd, Ingo and Busch, Niko A. and K{\"o}rner, Ursula and Schadow, Jeanette and Herrmann, Christoph S.},
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  journal = {Vision Research},
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}

@article{fu_2008,
  title = {When and Where Perceptual Load Interacts with Voluntary Visuospatial Attention: {{An}} Event-Related Potential and Dipole Modeling Study},
  shorttitle = {When and Where Perceptual Load Interacts with Voluntary Visuospatial Attention},
  author = {Fu, Shimin and Zinni, Marla and Squire, Peter N. and Kumar, Reshma and Caggiano, Daniel M. and Parasuraman, Raja},
  year = {2008},
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  journal = {NeuroImage},
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  number = {3},
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}

@article{fuhrmannalpert_2007,
  title = {Spatio-Temporal Information Analysis of Event-Related {{BOLD}} Responses},
  author = {Fuhrmann Alpert, Galit and Sun, Fellice T. and Handwerker, Daniel and D'Esposito, Mark and Knight, Robert T.},
  year = {2007},
  month = feb,
  journal = {NeuroImage},
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  number = {4},
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@inproceedings{fujimoto_2011,
  title = {System Identification Based on Variational {{Bayes}} Method and the Invariance under Coordinate Transformations},
  booktitle = {Decision and {{Control}} and {{European Control Conference}} ({{CDC-ECC}}), 2011 50th {{IEEE Conference}} On},
  author = {Fujimoto, Kenji and Satoh, Akinori and Fukunaga, Shuichi},
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@inproceedings{furmston_2009,
  title = {Solving Deterministic Policy (Po) Mpds Using Expectation-Maximisation and Antifreeze},
  booktitle = {Workshop on {{Learning}} and {{Data Mining}} for {{Robotics}}, {{Bled}}, {{Slovenia}}},
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}

@misc{fusseder_2018,
  title = {\"Ubersicht \"Uber {{Datentypen}} in {{R}}},
  author = {Fu{\ss}eder, Walter},
  year = {2018},
  journal = {JBerries}
}

@article{gabillon_,
  ids = {gabillon2012},
  title = {Best {{Arm Identification}}: {{A Unified Approach}} to {{Fixed Budget}} and {{Fixed Confidence}}},
  author = {Gabillon, Victor and Ghavamzadeh, Mohammad and Lazaric, Alessandro},
  pages = {9},
  abstract = {We study the problem of identifying the best arm(s) in the stochastic multi-armed bandit setting. This problem has been studied in the literature from two different perspectives: fixed budget and fixed confidence. We propose a unifying approach that leads to a meta-algorithm called unified gap-based exploration (UGapE), with a common structure and similar theoretical analysis for these two settings. We prove a performance bound for the two versions of the algorithm showing that the two problems are characterized by the same notion of complexity. We also show how the UGapE algorithm as well as its theoretical analysis can be extended to take into account the variance of the arms and to multiple bandits. Finally, we evaluate the performance of UGapE and compare it with a number of existing fixed budget and fixed confidence algorithms.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RXFSDP6N\\Gabillon et al. - Best Arm Identification A Unified Approach to Fix.pdf}
}

@article{gabriel_1969,
  title = {Simultaneous {{Test Procedures--Some Theory}} of {{Multiple Comparisons}}},
  author = {Gabriel, K. R.},
  year = {1969},
  month = feb,
  journal = {The Annals of Mathematical Statistics},
  volume = {40},
  number = {1},
  pages = {224--250},
  issn = {0003-4851, 2168-8990},
  abstract = {When a hypothesis is tested by a significance test and is not rejected, it is generally agreed that all hypotheses implied by that hypothesis (its "components") must also be considered as non-rejected. However, when the hypothesis is rejected the question remains as to which components may also be rejected. Various writers have given attention to this question and have proposed a variety of multiple comparisons methods based either on tests of each one of the components or on simultaneous confidence bounds on parametric functions related to the various hypotheses. An approach to such methods, apparently originally due to Tukey [27], is to test each component hypothesis by comparing its statistic with the {$\alpha\alpha\backslash$}alpha level critical value of the statistic for the overall hypothesis. This is called a Simultaneous Test Procedure (STP for short) as all hypotheses may be tested simultaneously and without reference to one another. An STP involves no stepwise testing of the kind employed by some other methods of multiple comparisons for means, in which subsets are tested for equality only if they are contained in sets which have already been found significant. (See [3], [4], [10], [18]). A general formalization of STP's is attempted in this paper. Section 2 introduces the requisite concepts of families of hypotheses and the implication relations between them, as well as the monotonicity relations between the related statistics. Section 3 defines STP's and shows conditions for coherence and consonance of their decisions, these properties being that hypothesis implication relations are preserved in the decisions of the STP. Section 4 discusses comparison of various STP's for the same hypotheses and shows the advantages of the union-intersection type of statistics and of reducing the family of hypotheses tested as much as possible. Section 5 translates all these results to simultaneous confidence statements after introducing the definitions necessary to allow such translation. The analogy between simultaneous test and confidence methods is of special importance as it brings a wide spectrum of methods within this framework, most of which was originally formulated in confidence region terms. This covers the original work of Tukey [27] and Scheffe [25] and continues with that of Roy and his associates [21] and most recently Krishnaiah [12], [13]. A general discussion of this confidence approach has been given by Aitchison [1] since the first draft of the present paper. In view of the close analogies pointed out in Section 5, it is not surprising that Aitchison arrives at the requirement of a constant critical value for all his tests, exactly as in an STP. In fact his approach and the present one are complementary. The formal theory is illustrated with ANOVA examples to clarify the concepts. No essentially new techniques are presented in this paper though this approach has been used elsewhere by the author to derive a number of practically useful procedures [6], [7], [8].},
  langid = {english},
  mrnumber = {MR240931},
  zmnumber = {0198.23602},
  annotation = {00383},
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}

@article{gannon_2019,
  title = {Blending {{Bayesian}} and {{Classical Tools}} to {{Define Optimal Sample-Size-Dependent Significance Levels}}},
  author = {Gannon, Mark Andrew and {de Bragan{\c c}a Pereira}, Carlos Alberto and Polpo, Adriano},
  year = {2019},
  month = mar,
  journal = {The American Statistician},
  volume = {73},
  number = {sup1},
  pages = {213--222},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2018.1518268},
  abstract = {This article argues that researchers do not need to completely abandon the p-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman\textendash Pearson Lemma that minimizes a linear combination of {$\alpha$}, the probability of rejecting a true null hypothesis, and {$\beta$}, the probability of failing to reject a false null, instead of fixing {$\alpha$} and minimizing {$\beta$}. The resulting hypothesis tests do not violate the Likelihood Principle and do not require any constraints on the dimensionalities of the sample space and parameter space. The procedure includes an ordering of the entire sample space and uses predictive probability (density) functions, allowing for testing of both simple and compound hypotheses. Accessible examples are presented to highlight specific characteristics of the new tests.},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MP97XDBD\\Gannon et al. - 2019 - Blending Bayesian and Classical Tools to Define Op.pdf}
}

@article{gao_2012,
  title = {Implementing the {{Nelder-Mead}} Simplex Algorithm with Adaptive Parameters},
  author = {Gao, Fuchang and Han, Lixing},
  year = {2012},
  month = jan,
  journal = {Computational Optimization and Applications},
  volume = {51},
  number = {1},
  pages = {259--277},
  issn = {0926-6003, 1573-2894},
  doi = {10.1007/s10589-010-9329-3},
  abstract = {In this paper, we first prove that the expansion and contraction steps of the Nelder-Mead simplex algorithm possess a descent property when the objective function is uniformly convex. This property provides some new insights on why the standard Nelder-Mead algorithm becomes inefficient in high dimensions. We then propose an implementation of the Nelder-Mead method in which the expansion, contraction, and shrink parameters depend on the dimension of the optimization problem. Our numerical experiments show that the new implementation outperforms the standard Nelder-Mead method for high dimensional problems.},
  langid = {english},
  annotation = {00320},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WTWSQAC2\\Implementing_the_Nelder-Mead_simplex_algorithm_wit.pdf}
}

@article{gardner_2019,
  title = {Optimality and Heuristics in Perceptual Neuroscience},
  author = {Gardner, Justin L.},
  year = {2019},
  month = apr,
  journal = {Nature Neuroscience},
  volume = {22},
  number = {4},
  pages = {514--523},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-019-0340-4},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8LPLHYNK\\Gardner - 2019 - Optimality and heuristics in perceptual neuroscien.pdf}
}

@article{garrido_,
  title = {Supplemental {{Information Functional Evidence}} for a {{Dual Route}} to {{Amygdala}}},
  author = {Garrido, Marta I. and Barnes, Gareth R. and Sahani, Maneesh and Dolan, Raymond J.},
  annotation = {00000},
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}

@article{garrido_2007,
  title = {Dynamic Causal Modelling of Evoked Potentials: {{A}} Reproducibility Study},
  shorttitle = {Dynamic Causal Modelling of Evoked Potentials},
  author = {Garrido, Marta I. and Kilner, James M. and Kiebel, Stefan J. and Stephan, Klaas E. and Friston, Karl J.},
  year = {2007},
  month = jul,
  journal = {NeuroImage},
  volume = {36},
  number = {3},
  pages = {571--580},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.03.014},
  langid = {english},
  annotation = {00165},
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}

@article{garrido_2007a,
  title = {Evoked Brain Responses Are Generated by Feedback Loops},
  author = {Garrido, Marta I. and Kilner, James M. and Kiebel, Stefan J. and Friston, Karl J.},
  year = {2007},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {104},
  number = {52},
  pages = {20961--20966},
  annotation = {00185},
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}

@article{garrido_2008,
  title = {The Functional Anatomy of the {{MMN}}: {{A DCM}} Study of the Roving Paradigm},
  shorttitle = {The Functional Anatomy of the {{MMN}}},
  author = {Garrido, Marta I. and Friston, Karl J. and Kiebel, Stefan J. and Stephan, Klaas E. and Baldeweg, Torsten and Kilner, James M.},
  year = {2008},
  month = aug,
  journal = {NeuroImage},
  volume = {42},
  number = {2},
  pages = {936--944},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.05.018},
  langid = {english},
  annotation = {00206},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\49HUHFWM\\Garrido et al_2008_NeuroImage_The functional anatomy of the MMN.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UFFZJKFX\\Garrido et al_2008_NeuroImage_The functional anatomy of the MMN.pdf}
}

@article{garrido_2009,
  title = {Dynamic {{Causal Modeling}} of the {{Response}} to {{Frequency Deviants}}},
  author = {Garrido, Marta I. and Kilner, James M. and Kiebel, Stefan J. and Friston, Karl J.},
  year = {2009},
  month = may,
  journal = {Journal of Neurophysiology},
  volume = {101},
  number = {5},
  pages = {2620--2631},
  issn = {0022-3077, 1522-1598},
  doi = {10.1152/jn.90291.2008},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HGSJ5C4S\\Garrido et al_2009_Journal of Neurophysiology_Dynamic Causal Modeling of the Response to Frequency Deviants.pdf}
}

@article{garrido_2009a,
  title = {Repetition Suppression and Plasticity in the Human Brain},
  author = {Garrido, Marta I. and Kilner, James M. and Kiebel, Stefan J. and Stephan, Klaas E. and Baldeweg, Torsten and Friston, Karl J.},
  year = {2009},
  month = oct,
  journal = {NeuroImage},
  volume = {48},
  number = {1},
  pages = {269--279},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.06.034},
  langid = {english},
  annotation = {00104},
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}

@article{garrido_2009b,
  title = {The Mismatch Negativity: {{A}} Review of Underlying Mechanisms},
  shorttitle = {The Mismatch Negativity},
  author = {Garrido, Marta I. and Kilner, James M. and Stephan, Klaas E. and Friston, Karl J.},
  year = {2009},
  month = mar,
  journal = {Clinical Neurophysiology},
  volume = {120},
  number = {3},
  pages = {453--463},
  issn = {13882457},
  doi = {10.1016/j.clinph.2008.11.029},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CZXFJZR2\\Garrido et al_2009_Clinical Neurophysiology_The mismatch negativity.pdf}
}

@article{garrido_2012,
  title = {Functional {{Evidence}} for a {{Dual Route}} to {{Amygdala}}},
  author = {Garrido, Marta~I. and Barnes, Gareth~R. and Sahani, Maneesh and Dolan, Raymond~J.},
  year = {2012},
  month = jan,
  journal = {Current Biology},
  volume = {22},
  number = {2},
  pages = {129--134},
  issn = {09609822},
  doi = {10.1016/j.cub.2011.11.056},
  langid = {english},
  annotation = {00068},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DB8Z8H8S\\Garrido et al_2012_Current Biology_Functional Evidence for a Dual Route to Amygdala.pdf;C\:\\Users\\dirko\\Zotero\\storage\\JAMP6BRE\\Garrido et al_2012_Current Biology_Functional Evidence for a Dual Route to Amygdala.pdf}
}

@article{garrison_2016,
  title = {A Systematic Literature Review of Individuals' Perspectives on Broad Consent and Data Sharing in the {{United States}}},
  author = {Garrison, Nanibaa' A. and Sathe, Nila A. and Antommaria, Armand H. Matheny and Holm, Ingrid A. and Sanderson, Saskia C. and Smith, Maureen E. and McPheeters, Melissa L. and Clayton, Ellen W.},
  year = {2016},
  month = jul,
  journal = {Genetics in Medicine},
  volume = {18},
  number = {7},
  pages = {663--671},
  issn = {1098-3600, 1530-0366},
  doi = {10.1038/gim.2015.138},
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}

@article{gau_2016,
  title = {How Prior Expectations Shape Multisensory Perception},
  author = {Gau, Remi and Noppeney, Uta},
  year = {2016},
  journal = {NeuroImage},
  volume = {124},
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}

@book{gazzaniga_2000,
  title = {The New Cognitive Neurosciences},
  editor = {Gazzaniga, Michael S.},
  year = {2000},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-07195-6},
  lccn = {QP360.5 .N4986 2000},
  keywords = {Brain,Cognitive neuroscience,Mental Processes,physiology},
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}

@book{gazzaniga_2013,
  title = {Cognitive Neuroscience: The Biology of the Mind},
  shorttitle = {Cognitive Neuroscience},
  author = {Gazzaniga, Michael S. and Ivry, Richard B. and Mangun, G. R.},
  year = {2013},
  edition = {Fourth edition},
  publisher = {{W. W. Norton \& Company, Inc}},
  address = {{New York, N.Y}},
  isbn = {978-0-393-91348-4},
  langid = {english},
  lccn = {QP360.5 .G39 2013},
  keywords = {Cognitive neuroscience},
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}

@article{ge_,
  ids = {geffner_solving_1998},
  title = {Solving {{Large POMDPs}} Using {{Real Time Dynamic Programming}}},
  author = {Ge, Hector and Bonet, Blai},
  pages = {20},
  abstract = {Partially Observable Markov Decision Processes (pomdps) are general models of sequential decision problems in which both actions and observations may be noisy. Many problems of interest can be formulated as pomdps yet the use of pomdps has been limited by the lack of e ective algorithms: optimal algorithms don't scale up and heuristic algorithms often do poorly. In this paper, a new pomdp algorithm is introduced that combines the bene ts of optimal and heuristic procedures producing good solutions quickly even in problems that are large. Like optimal procedures, the procedure rtdp-bel attempts to solve the information mdp, yet like heuristic procedures, it makes decisions in real time following a suitable heuristic function. rtdp-bel is a Real Time Dynamic Programming algorithm 1], namely, a greedy search algorithm that learns to solve mdps by repeatedly updating the heuristic values of the states that are visited. As shown by Barto et al. such updates eventually deliver an optimal behavior provided that the state space is nite and the initial heuristic values are admissible. Since information mdps have in nite state spaces we discretize probabilities and combine them with heuristic values obtained from the underlying mdp. Although the resulting algorithm is not guaranteed to be optimal, experiments over a number of benchmarks suggest that large pomdps are quickly and consistently solved, and that solutions, if not optimal, tend to be very good.},
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}

@article{gebhardt_2008,
  title = {Synchronized Measurement of Simultaneous {{EEG}}\textendash{{fMRI}}: {{A}} Simulation Study},
  shorttitle = {Synchronized Measurement of Simultaneous {{EEG}}\textendash{{fMRI}}},
  author = {Gebhardt, Helge and Blecker, Carlo R. and Bischoff, Matthias and Morgen, Katrin and Oschmann, Patrick and Vaitl, Dieter and Sammer, Gebhard},
  year = {2008},
  month = dec,
  journal = {Clinical Neurophysiology},
  volume = {119},
  number = {12},
  pages = {2703--2711},
  issn = {13882457},
  doi = {10.1016/j.clinph.2008.09.018},
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}

@article{geerligs_2021,
  title = {Improving the Sensitivity of Cluster-based Statistics for Functional Magnetic Resonance Imaging Data},
  author = {Geerligs, Linda and Maris, Eric},
  year = {2021},
  month = jun,
  journal = {Human Brain Mapping},
  volume = {42},
  number = {9},
  pages = {2746--2765},
  issn = {1065-9471, 1097-0193},
  doi = {10.1002/hbm.25399},
  abstract = {Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster-based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false-alarm rate, (b) that the sensitivity profiles of cluster-based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.},
  langid = {english},
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}

@article{gehring_1993,
  title = {A {{Neural System}} for {{Error Detection}} and {{Compensation}}},
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}

@article{geisser_1958,
  title = {An {{Extension}} of {{Box}}'s {{Results}} on the {{Use}} of the \${{F}}\$ {{Distribution}} in {{Multivariate Analysis}}},
  author = {Geisser, Seymour and Greenhouse, Samuel W.},
  year = {1958},
  journal = {The Annals of Mathematical Statistics},
  volume = {29},
  number = {3},
  pages = {885--891},
  issn = {0003-4851},
  abstract = {The mixed model in a 2-way analysis of variance is characterized by a fixed classification, e.g., treatments, and a random classification, e.g., plots or individuals. If we consider \$k\$ different treatments each applied to everyone of \$n\$ individuals, and assume the usual analysis of variance assumptions of uncorrelated errors, equal variances and normality, an appropriate analysis for the set of \$nk\$ observations \$x\_\{ij\}, i = 1, 2, \textbackslash cdots n, j = 1, 2, \textbackslash cdots k\$, is ???? where the \$F\$ ratio under the null hypothesis has the \$F\$ distribution with \$(k - 1)\$ and \$(k - 1)(n - 1)\$ degrees of freedom. As is well known, if we extend the situation so that the errors have equal correlations instead of being uncorrelated, the \$F\$ ratio has the same distribution. Under the null hypothesis, the numerator estimates the same quantity as the denominator, namely, \$(1 - \textbackslash rho)\textbackslash sigma\^2\$, where \$\textbackslash rho\$ is the constant correlation coefficient among the treatments. This case can also be considered as a sampling of \$n\$ vectors (individuals) from a \$k\$-variate normal population with variance-covariance matrix \$\$V = \textbackslash sigma\^2 \textbackslash begin\{pmatrix\} 1 \& \textbackslash rho \& \textbackslash cdots \& \textbackslash rho \textbackslash\textbackslash{} \textbackslash rho \& \& \& \textbackslash vdots \textbackslash\textbackslash{} \textbackslash vdots \& \& \& \textbackslash rho \textbackslash\textbackslash{} \textbackslash rho \& \textbackslash cdots \& \textbackslash rho \& 1\textbackslash end\{pmatrix\}.\$\$ If we consider this type of formulation and suppose the \$k\$ treatment errors to have a multivariate normal distribution with unknown variance-covariance matrix (the same for each individual), then the usual test described above is valid for \$k = 2\$. For \$k {$>$} 2\$, and \$n \textbackslash geqq k\$, Hotelling's \$T\^2\$ is the appropriate test for the homogeneity of the treatment means. However, the working statistician is sometimes confronted with the case where \$k {$>$} n\$, or he does not have the adequate means for computing large order inverse matrices and would therefore like to use the original test ratio which in general does not have the requisite \$F\$ distribution. Box [1] and [2] has given an approximate distribution of the test ratio to be \$F\textbackslash lbrack(k - 1)\textbackslash epsilon, (k - 1)(n - 1)\textbackslash epsilon\textbackslash rbrack\$ where \$\textbackslash epsilon\$ is a function of the population variances and covariances and may further be approximated by the sample variances and covariances. We show in Section 3 that \$\textbackslash epsilon \textbackslash geqq (k - 1)\^\{-1\}\$, and therefore a conservative test would be \$F(1, n - 1)\$. Box referred only to one group of \$n\$ individuals. We shall extend his results to a frequently occurring case, namely, the analysis of \$g\$ groups where the \$\textbackslash alpha\$th group has \$n\_\textbackslash alpha\$ individuals, \$\textbackslash alpha = 1, 2, \textbackslash cdots g\$, and \$\textbackslash Sigma\^g\_\{\textbackslash alpha = 1\} n\_\textbackslash alpha = N\$. We will show that the treatment mean square and the treatment \$\textbackslash times\$ group interaction can be tested in the same approximate fashion by using the Box procedure.},
  annotation = {01183}
}

@article{genon_2018,
  title = {How to {{Characterize}} the {{Function}} of a {{Brain Region}}},
  author = {Genon, Sarah and Reid, Andrew and Langner, Robert and Amunts, Katrin and Eickhoff, Simon B.},
  year = {2018},
  month = feb,
  journal = {Trends in Cognitive Sciences},
  issn = {13646613},
  doi = {10.1016/j.tics.2018.01.010},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\R2FS67PG\\Genon et al_2018_Trends in Cognitive Sciences_How to Characterize the Function of a Brain Region.pdf}
}

@article{genovese_2002,
  title = {Thresholding of {{Statistical Maps}} in {{Functional Neuroimaging Using}} the {{False Discovery Rate}}},
  author = {Genovese, Christopher R. and Lazar, Nicole A. and Nichols, Thomas},
  year = {2002},
  month = apr,
  journal = {NeuroImage},
  volume = {15},
  number = {4},
  pages = {870--878},
  issn = {10538119},
  doi = {10.1006/nimg.2001.1037},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\G8PAHZSN\\Genovese et al_2002_NeuroImage_Thresholding of Statistical Maps in Functional Neuroimaging Using the False.pdf}
}

@article{georgee.monahan_1980,
  title = {Optimal {{Stopping}} in a {{Partially Observable Markov Process}} with {{Costly Information}}},
  author = {George E. Monahan},
  year = {1980},
  journal = {Operations Research},
  volume = {28},
  number = {6},
  pages = {1319--1334},
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}

@article{georgee.monahan_1982,
  title = {A {{Survey}} of {{Partially Observable Markov Decision Processes}}: {{Theory}}, {{Models}}, and {{Algorithms}}},
  author = {George E. Monahan},
  year = {1982},
  journal = {Management Science},
  volume = {28},
  number = {1},
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}

@article{georgie_2018,
  title = {A Perceptual Decision Making {{EEG}}/{{fMRI}} Data Set},
  author = {Georgie, Yasmin K and Porcaro, Camillo and Mayhew, Stephen D and Bagshaw, Andrew P and Ostwald, Dirk},
  year = {2018},
  pages = {13},
  abstract = {We present a neuroimaging data set comprising behavioural, electroencephalographic (EEG), and functional magnetic resonance imaging (fMRI) data that were acquired from human subjects performing a perceptual decision making task. EEG data were acquired both independently and simultaneously with fMRI data. Potential data usages include the validation of biocomputational accounts of human perceptual decision making or the empirical validation of simultaneous EEG/fMRI data processing algorithms. The dataset is available from the Open Science Framework and organized according to the Brain Imaging Data Structure standard.},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EW6PPHCW\\Georgie et al. - 2018 - A perceptual decision making EEGfMRI data set.pdf;C\:\\Users\\dirko\\Zotero\\storage\\IGJXV8QN\\Georgie et al. - A perceptual decision making EEGfMRI data set.pdf}
}

@book{georgii_2009,
  title = {{Stochastik: Einf\"uhrung in die Wahrscheinlichkeitstheorie und Statistik}},
  shorttitle = {{Stochastik}},
  author = {Georgii, Hans-Otto},
  year = {2009},
  series = {{De-Gruyter-Lehrbuch}},
  edition = {4., \"uberarb. und erw. Aufl},
  publisher = {{de Gruyter}},
  address = {{Berlin}},
  abstract = {Verlagstext: Infolge der ausgesprochen positiven Aufnahme dieses Lehrbuches liegt nun die 4. Auflage vor, die nochmals weiter bearbeitet und erg\"anzt wurde. Das Buch gibt eine Einf\"uhrung in die zentralen Ideen und Ergebnisse der Wahrscheinlichkeitstheorie und Statistik. Die stochastischen Konzepte, Modelle und Methoden werden durch typische Anwendungsbeispiele motiviert und anschlie\ss end theoretisch analysiert und systematisch entwickelt. Mit einem Stoffumfang von etwa zwei Semestern vermittelt das Buch ein solides Fundament an Kenntnissen, verwendet jedoch nur wenig Ma\ss theorie und wahrt dadurch seinen einf\"uhrenden Charakter. Zahlreiche \"Ubungsaufgaben illustrieren und erg\"anzen den Stoff. Neu in der 4. Auflage sind L\"osungsskizzen f\"ur einen Teil der Aufgaben},
  isbn = {978-3-11-021526-7},
  langid = {german},
  annotation = {OCLC: 428822757},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\W2F6IC7I\\Georgii - 2009 - Stochastik Einführung in die Wahrscheinlichkeitst.pdf}
}

@article{gerdts_,
  title = {Management {{Mathematics Group School}} of {{Mathematics University}} of {{Birmingham}}},
  author = {Gerdts, Matthias},
  pages = {249},
  annotation = {00000},
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}

@book{gerrig_2020,
  title = {{Psychologie}},
  author = {Gerrig, Richard J. and Zimbardo, Philip G. and Graf, Ralf and Gerrig, Richard J.},
  year = {2020},
  series = {{ps psychologie}},
  edition = {18., aktualis. Aufl., [Nachdr.]},
  publisher = {{Pearson Studium}},
  address = {{M\"unchen}},
  isbn = {978-3-8273-7275-8},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\N5KY4N45\\Gerrig et al. - Psychologie.pdf}
}

@article{gershman_2010,
  title = {Learning Latent Structure: Carving Nature at Its Joints},
  shorttitle = {Learning Latent Structure},
  author = {Gershman, Samuel J and Niv, Yael},
  year = {2010},
  month = apr,
  journal = {Current Opinion in Neurobiology},
  volume = {20},
  number = {2},
  pages = {251--256},
  issn = {09594388},
  doi = {10.1016/j.conb.2010.02.008},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KI5X9PU5\\Gershman_Niv_2010_Current Opinion in Neurobiology_Learning latent structure.pdf}
}

@article{gershman_2015,
  title = {A {{Unifying Probabilistic View}} of {{Associative Learning}}},
  author = {Gershman, Samuel J.},
  editor = {Diedrichsen, J{\"o}rn},
  year = {2015},
  month = nov,
  journal = {PLOS Computational Biology},
  volume = {11},
  number = {11},
  pages = {e1004567},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1004567},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AL6XLPJZ\\Gershman - 2015 - A Unifying Probabilistic View of Associative Learn.pdf;C\:\\Users\\dirko\\Zotero\\storage\\NWNN386X\\Gershman - 2015 - A Unifying Probabilistic View of Associative Learn.pdf}
}

@article{gershman_2018,
  ids = {gershman2018},
  title = {Deconstructing the Human Algorithms for Exploration},
  author = {Gershman, Samuel J.},
  year = {2018},
  month = apr,
  journal = {Cognition},
  volume = {173},
  pages = {34--42},
  publisher = {{Elsevier}},
  issn = {00100277},
  doi = {10.1016/j.cognition.2017.12.014},
  abstract = {The dilemma between information gathering (exploration) and reward seeking (exploitation) is a fundamental problem for reinforcement learning agents. How humans resolve this dilemma is still an open question, because experiments have provided equivocal evidence about the underlying algorithms used by humans. We show that two families of algorithms can be distinguished in terms of how uncertainty affects exploration. Algorithms based on uncertainty bonuses predict a change in response bias as a function of uncertainty, whereas algorithms based on sampling predict a change in response slope. Two experiments provide evidence for both bias and slope changes, and computational modeling confirms that a hybrid model is the best quantitative account of the data.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LGBIMWTE\\Gershman - 2018 - Deconstructing the human algorithms for exploratio.pdf}
}

@article{gershman_2018a,
  title = {Uncertainty and Exploration},
  author = {Gershman, Samuel J},
  year = {2018},
  month = jul,
  doi = {10.1101/265504},
  abstract = {In order to discover the most rewarding actions, agents must collect information about their environment, potentially foregoing reward. The optimal solution to this "explore-exploit" dilemma is often computationally challenging, but principled algorithmic approximations exist. These approximations utilize uncertainty about action values in different ways. Some random exploration algorithms scale the level of choice stochasticity with the level of uncertainty. Other directed exploration algorithms add a "bonus" to action values with high uncertainty. Random exploration algorithms are sensitive to total uncertainty across actions, whereas directed exploration algorithms are sensitive to relative uncertainty. This paper reports a multi-armed bandit experiment in which total and relative uncertainty were orthogonally manipulated. We found that humans employ both exploration strategies, and that these strategies are independently controlled by different uncertainty computations.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5VU4XRFL\\Gershman - 2018 - Uncertainty and exploration.pdf}
}

@article{gershman_2019,
  ids = {gershman2019,gershmanUncertaintyExploration2019a},
  title = {Uncertainty and Exploration.},
  author = {Gershman, Samuel J.},
  year = {2019},
  month = jul,
  journal = {Decision},
  volume = {6},
  number = {3},
  pages = {277--286},
  publisher = {{Educational Publishing Foundation}},
  address = {{US}},
  issn = {2325-9973, 2325-9965},
  doi = {10.1037/dec0000101},
  langid = {english},
  keywords = {*Algorithms,*Learning,*Reinforcement,*Rewards,*Uncertainty,Environment,Inference,Statistical Probability},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AFQT8TCE\\Gershman - 2019 - Uncertainty and exploration.pdf;C\:\\Users\\dirko\\Zotero\\storage\\MCNJW6V3\\Gershman - 2019 - Uncertainty and exploration..pdf}
}

@article{geuter_2018,
  title = {Effect {{Size}} and {{Power}} in {{fMRI Group Analysis}}},
  author = {Geuter, Stephan and Qi, Guanghao and Welsh, Robert C. and Wager, Tor D. and Lindquist, Martin A.},
  year = {2018},
  month = apr,
  journal = {bioRxiv},
  doi = {10.1101/295048},
  abstract = {Multi-subject functional magnetic resonance imaging (fMRI) analysis is often concerned with determining whether there exists a significant population-wide 'activation' in a comparison between two or more conditions. Typically this is assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. In this work we investigate several aspects of this type of analysis. First, we study the e{$\carriagereturn$}ects of sample size on the sensitivity and reliability of the group analysis, allowing us to evaluate the ability of small sampled studies to e{$\carriagereturn$}ectively capture population-level e{$\carriagereturn$}ects of interest. Second, we assess the di{$\carriagereturn$}erence in sensitivity and reliability when using volumetric or surface based data. Third, we investigate potential biases in estimating e{$\carriagereturn$}ect sizes as a function of sample size. To perform this analysis we utilize the task-based fMRI data from the 500-subject release from the Human Connectome Project (HCP). We treat the complete collection of subjects (N = 491) as our population of interest, and perform a single-subject analysis on each subject in the population. We investigate the ability to recover population level e{$\carriagereturn$}ects using a subset of the population and standard analytical techniques. Our study shows that sample sizes of 40 are generally able to detect regions with high e{$\carriagereturn$}ect sizes (Cohen's d {$>$} 0.8), while sample sizes closer to 80 are required to reliably recover regions with medium e{$\carriagereturn$}ect sizes (0.5 {$<$} d {$<$} 0.8). We find little di{$\carriagereturn$}erence in results when using volumetric or surface based data with respect to standard mass-univariate group analysis. Finally, we conclude that special care is needed when estimating e{$\carriagereturn$}ect sizes, particularly for small sample sizes.},
  langid = {english},
  annotation = {00002},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DXRE3QXV\\Geuter et al. - 2018 - Effect Size and Power in fMRI Group Analysis.pdf}
}

@article{ghahramani_,
  title = {Factorial {{Learning}} and the {{EM Algorithm}}},
  author = {Ghahramani, Zoubin},
  pages = {8},
  abstract = {Many real world learning problems are best characterized by an interaction of multiple independent causes or factors. Discovering such causal structure from the data is the focus of this paper. Based on Zemel and Hinton's cooperative vector quantizer (CVQ) architecture, an unsupervised learning algorithm is derived from the Expectation\{Maximization (EM) framework. Due to the combinatorial nature of the data generation process, the exact E-step is computationally intractable. Two alternative methods for compapuptirnogximthaetiEo-ns,taepndasroemperopproomseids:ingGeimbbpsirsiacamlprelisnugltsanadre mpreeasnen-teedld.\vphantom\}},
  langid = {english},
  annotation = {00134},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\494UHB85\\Ghahramani - Factorial Learning and the EM Algorithm.pdf}
}

@article{ghahramani_2015,
  title = {Probabilistic Machine Learning and Artificial Intelligence},
  author = {Ghahramani, Zoubin},
  year = {2015},
  month = may,
  journal = {Nature},
  volume = {521},
  number = {7553},
  pages = {452--459},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature14541},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4BW8XFG9\\Ghahramani_2015_Nature_Probabilistic machine learning and artificial intelligence.pdf}
}

@article{ghahramani_a,
  title = {Parameter {{Estimation}} for {{Linear Dynamical Systems}}},
  author = {Ghahramani, Zoubin},
  pages = {6},
  abstract = {Linear systems have been used extensively in engineering to model and control the behavior of dynamical systems. In this note, we present the Expectation Maximization (EM) algorithm for estimating the parameters of linear systems (Shumway and Sto er, 1982). We also point out the relationship between linear dynamical systems, factor analysis, and hidden Markov models.},
  langid = {english},
  annotation = {00627},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RDLIUNGX\\Ghahramani - Parameter Estimation for Linear Dynamical Systems.pdf}
}

@article{ghahramani_b,
  ids = {ghahramaniEMAlgorithmMixtures},
  title = {The {{EM Algorithm}} for {{Mixtures}} of {{Factor Analyzers}}},
  author = {Ghahramani, Zoubin},
  pages = {8},
  abstract = {Factor analysis, a statistical method for modeling the covariance structure of high dimensional data using a small number of latent variables, can be extended by allowing di erent local factor models in di erent regions of the input space. This results in a model which concurrently performs clustering and dimensionality reduction, and can be thought of as a reduced dimension mixture of Gaussians. We present an exact Expectation\{Maximization algorithm for tting the parameters of this mixture of factor analyzers.\vphantom\}},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3FU3ZT7P\\Ghahramani - The EM Algorithm for Mixtures of Factor Analyzers.pdf;C\:\\Users\\dirko\\Zotero\\storage\\VGZ6AXBT\\Ghahramani - The EM Algorithm for Mixtures of Factor Analyzers.pdf}
}

@article{gherman_2015,
  title = {Neural Representations of Confidence Emerge from the Process of Decision Formation during Perceptual Choices},
  author = {Gherman, Sabina and Philiastides, Marios G.},
  year = {2015},
  month = feb,
  journal = {NeuroImage},
  volume = {106},
  pages = {134--143},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2014.11.036},
  langid = {english},
  annotation = {00030},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8THDXV4C\\Gherman_Philiastides_2015_NeuroImage_Neural representations of confidence emerge from the process of decision.pdf;C\:\\Users\\dirko\\Zotero\\storage\\DZUFASQ6\\Gherman_Philiastides_2015_NeuroImage_Neural representations of confidence emerge from the process of decision.pdf}
}

@book{ghosh_2003,
  title = {Bayesian Nonparametrics},
  author = {Ghosh, J. K. and Ramamoorthi, R. V.},
  year = {2003},
  series = {Springer Series in Statistics},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-95537-7},
  langid = {english},
  lccn = {QA279.5 .G48 2003},
  keywords = {Bayesian statistical decision theory,Nonparametric statistics},
  annotation = {00778},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JY2QD3BQ\\Ghosh und Ramamoorthi - 2003 - Bayesian nonparametrics.pdf}
}

@article{gianola_1986,
  title = {Bayesian {{Methods}} in {{Animal Breeding Theory}}},
  author = {Gianola, Daniel and Fernando, Rohan L.},
  year = {1986},
  month = jul,
  journal = {Journal of Animal Science},
  volume = {63},
  number = {1},
  pages = {217--244},
  issn = {0021-8812, 1525-3163},
  doi = {10.2527/jas1986.631217x},
  langid = {english},
  annotation = {00393},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QIPD4N5C\\Gianola und Fernando - 1986 - Bayesian Methods in Animal Breeding Theory.pdf}
}

@book{gibbons_2003,
  title = {Nonparametric Statistical Inference},
  author = {Gibbons, Jean Dickinson and Chakraborti, Subhabrata},
  year = {2003},
  series = {Statistics, Textbooks and Monographs},
  edition = {4th ed., rev. and expanded},
  number = {v. 168},
  publisher = {{Marcel Dekker}},
  address = {{New York}},
  isbn = {978-0-8247-4052-8},
  langid = {english},
  lccn = {QA278.8 .G498 2003},
  keywords = {Nonparametric statistics},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2MVQ2AGY\\Gibbons und Chakraborti - 2003 - Nonparametric statistical inference.pdf}
}

@article{gibbs_2002,
  title = {On {{Choosing}} and {{Bounding Probability Metrics}}},
  author = {Gibbs, Alison L. and Su, Francis Edward},
  year = {2002},
  month = dec,
  journal = {International Statistical Review / Revue Internationale de Statistique},
  volume = {70},
  number = {3},
  pages = {419},
  issn = {03067734},
  doi = {10.2307/1403865},
  abstract = {When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric.},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5XYZ556I\\Gibbs and Su - 2002 - On Choosing and Bounding Probability Metrics.pdf}
}

@inproceedings{giffin_2007,
  title = {Updating {{Probabilities}} with {{Data}} and {{Moments}}},
  author = {Giffin, Adom and Caticha, Ariel and Knuth, Kevin H. and Caticha, Ariel and Center, Julian L. and Giffin, Adom and Rodr{\'i}guez, Carlos C.},
  year = {2007},
  volume = {954},
  pages = {74--84},
  publisher = {{AIP}},
  doi = {10.1063/1.2821302},
  abstract = {We use the method of Maximum (relative) Entropy to process information in the form of observed data and moment constraints. The generic ``canonical'' form of the posterior distribution for the problem of simultaneous updating with data and moments is obtained. We discuss the general problem of non-commuting constraints, when they should be processed sequentially and when simultaneously. As an illustration, the multinomial example of die tosses is solved in detail for two superficially similar but actually very different problems.},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\MEB2MINQ\\Giffin et al. - 2007 - Updating Probabilities with Data and Moments.pdf}
}

@article{gigerenzer_1991,
  title = {From Tools to Theories: {{A}} Heuristic of Discovery in Cognitive Psychology.},
  shorttitle = {From Tools to Theories},
  author = {Gigerenzer, Gerd},
  year = {1991},
  journal = {Psychological Review},
  volume = {98},
  number = {2},
  pages = {254--267},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/0033-295X.98.2.254},
  langid = {english},
  annotation = {00000},
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}

@article{gigerenzer_2004,
  title = {Mindless Statistics},
  author = {Gigerenzer, Gerd},
  year = {2004},
  month = nov,
  journal = {The Journal of Socio-Economics},
  volume = {33},
  number = {5},
  pages = {587--606},
  issn = {10535357},
  doi = {10.1016/j.socec.2004.09.033},
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}

@article{gijsen_2021,
  title = {Neural Surprise in Somatosensory {{Bayesian}} Learning},
  author = {Gijsen, Sam and Grundei, Miro and Lange, Robert T. and Ostwald, Dirk and Blankenburg, Felix},
  editor = {Schwartenbeck, Philipp},
  year = {2021},
  month = feb,
  journal = {PLOS Computational Biology},
  volume = {17},
  number = {2},
  pages = {e1008068},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1008068},
  abstract = {Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VNW6K7ID\\Gijsen et al. - 2021 - Neural surprise in somatosensory Bayesian learning.pdf}
}

@book{gilboa_2009,
  title = {Theory of {{Decision}} under {{Uncertainty}}},
  author = {Gilboa, Itzhak},
  year = {2009},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge}},
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}

@article{gintis_2007,
  title = {A Framework for the Unification of the Behavioral Sciences},
  author = {Gintis, Herbert},
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  journal = {Behavioral and Brain Sciences},
  volume = {30},
  number = {01},
  issn = {0140-525X, 1469-1825},
  doi = {10.1017/S0140525X07000581},
  langid = {english},
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}

@article{gitelman_2003,
  title = {Modeling Regional and Psychophysiologic Interactions in {{fMRI}}: The Importance of Hemodynamic Deconvolution},
  shorttitle = {Modeling Regional and Psychophysiologic Interactions in {{fMRI}}},
  author = {Gitelman, Darren R. and Penny, William D. and Ashburner, John and Friston, Karl J.},
  year = {2003},
  month = may,
  journal = {NeuroImage},
  volume = {19},
  number = {1},
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  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00058-2},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\S6933RAU\\Gitelman et al_2003_NeuroImage_Modeling regional and psychophysiologic interactions in fMRI.pdf}
}

@article{gittins_,
  title = {Bandit {{Processes}} and {{Dynamic Allocation Indices}}},
  author = {Gittins, J C},
  pages = {31},
  abstract = {The paper aims to give a unified account of the central concepts in recent work on bandit processes and dynamic allocation indices; to show how these reduce some previously intractable problems to the problem of calculating such indices; and to describe how these calculations may be carried out. Applications to stochastic scheduling, sequential clinical trials and a class of search problems are discussed.},
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}

@article{gittins_1979,
  title = {A {{Dynamic Allocation Index}} for the {{Discounted Multiarmed Bandit Problem}}},
  author = {Gittins, J. C. and Jones, D. M.},
  year = {1979},
  month = dec,
  journal = {Biometrika},
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@book{gittins_2011,
  title = {Multi-Armed Bandit Allocation Indices},
  author = {Gittins, John C. and Weber, Richard and Glazebrook, Kevin D.},
  year = {2011},
  edition = {2nd ed},
  publisher = {{John Wiley \& Sons}},
  address = {{Hoboken, NJ}},
  isbn = {978-0-470-67002-6},
  lccn = {QA279 .G55 2011},
  keywords = {Mathematical models,Mathematical optimization,Programming (Mathematics),Resource allocation},
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}

@article{glascher_,
  title = {Contrast Weights in Flexible Factorial Design with Multiple Groups of Subjects},
  author = {Gl{\"a}scher, Jan and Gitelman, Darren},
  pages = {12},
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}

@article{glascher_2009,
  title = {Visualization of {{Group Inference Data}} in {{Functional Neuroimaging}}},
  author = {Gl{\"a}scher, Jan},
  year = {2009},
  month = mar,
  journal = {Neuroinformatics},
  volume = {7},
  number = {1},
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@article{glascher_2010,
  ids = {glascher_states_2010-1},
  title = {States versus {{Rewards}}: {{Dissociable Neural Prediction Error Signals Underlying Model-Based}} and {{Model-Free Reinforcement Learning}}},
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  author = {Gl{\"a}scher, Jan and Daw, Nathaniel and Dayan, Peter and O'Doherty, John P.},
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  journal = {Neuron},
  volume = {66},
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  issn = {08966273},
  doi = {10.1016/j.neuron.2010.04.016},
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  pmid = {20510862},
  keywords = {Sysneuro},
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}

@article{glass_1976,
  title = {Primary, {{Secondary}}, and {{Meta-Analysis}} of {{Research}}},
  author = {Glass, Gene V.},
  year = {1976},
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@article{glazer_2018,
  title = {Beyond the {{FRN}}: {{Broadening}} the Time-Course of {{EEG}} and {{ERP}} Components Implicated in Reward Processing},
  shorttitle = {Beyond the {{FRN}}},
  author = {Glazer, James E. and Kelley, Nicholas J. and Pornpattananangkul, Narun and Mittal, Vijay A. and Nusslock, Robin},
  year = {2018},
  month = oct,
  journal = {International Journal of Psychophysiology},
  volume = {132},
  pages = {184--202},
  issn = {01678760},
  doi = {10.1016/j.ijpsycho.2018.02.002},
  abstract = {Most reward-related electroencephalogram (EEG) studies focus exclusively on the feedback-related negativity (FRN, also known as feedback negativity or FN, medial-frontal negativity or MFN, feedback error-related negativity or fERN, and reward positivity or RewP). This component is usually measured approximately 200\textendash 300 ms post-feedback at a single electrode in the frontal-central area (e.g., Fz or FCz). The present review argues that this singular focus on the FRN fails to leverage EEG's greatest strength, its temporal resolution, by underutilizing the rich variety of event-related potential (ERP) and EEG time-frequency components encompassing the wider temporal heterogeneity of reward processing. The primary objective of this review is to provide a comprehensive understanding of often overlooked ERP and EEG correlates beyond the FRN in the context of reward processing with the secondary goal of guiding future research toward multistage experimental designs and multicomponent analyses that leverage the temporal power of EEG. We comprehensively review reward-related ERPs (including the FRN, readiness potential or RP, stimulus-preceding negativity or SPN, contingent-negative variation or CNV, cue-related N2 and P3, Feedback-P3, and late-positive potential or LPP/ slow-wave), and reward-related EEG time-frequency components (changes in power at alpha, beta, theta, and delta bands). These electrophysiological signatures display distinct time-courses, scalp topographies, and reflect independent psychological processes during anticipatory and/or outcome stages of reward processing. Special consideration is given to the time-course of each component and factors that significantly contribute to component variation. Concluding remarks identify current limitations along with recommendations for potential important future directions.},
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@book{glimcher_2014,
  title = {Neuroeconomics: Decision Making and the Brain},
  shorttitle = {Neuroeconomics},
  editor = {Glimcher, Paul W. and Fehr, Ernst},
  year = {2014},
  edition = {Second edition},
  publisher = {{Elsevier/AP, Academic Press is an imprint of Elsevier}},
  address = {{Amsterdam : Boston}},
  isbn = {978-0-12-416008-8},
  lccn = {QP360.5 .N477 2014},
  keywords = {Aspect cognitif,Aspect psychologique,Brain,Choice Behavior,Decision making,Decision Making,Economic man,Economics; Behavioral,Économie politique,Homo oeconomicus,Neuroeconomics,physiology,Prise de décision,Psychological aspects},
  annotation = {OCLC: ocn868677826},
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}

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}

@article{glover_2011,
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  abstract = {Functional MRI has enjoyed an exciting development course with an exponential growth in published studies since its inception in the early 1990s, and it has become commonplace for clinical uses such as presurgical planning, fundamental cognitive neuroscience investigations, behavior modification, and training. Informed by fMRI, more sophisticated modeling of brain networks is certain to lead to new levels of understanding of the human brain.},
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@article{gold_2007,
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@article{goldstein_2010,
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@article{gomez_2011,
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@article{gong_2018,
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@book{goodfellow_2017,
  title = {{Deep Learning}},
  author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
  year = {2017},
  month = jan,
  publisher = {{The Mit Press}},
  address = {{Cambridge, Massachusetts}},
  abstract = {"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.  Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.},
  isbn = {978-0-262-03561-3},
  langid = {Englisch},
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@article{goodman_2017,
  title = {De-Identified Genomic Data Sharing: The Research Participant Perspective},
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  author = {Goodman, Deborah and Johnson, Catherine O. and Bowen, Deborah and Smith, Megan and Wenzel, Lari and Edwards, Karen},
  year = {2017},
  month = jul,
  journal = {Journal of Community Genetics},
  volume = {8},
  number = {3},
  pages = {173--181},
  issn = {1868-310X, 1868-6001},
  doi = {10.1007/s12687-017-0300-1},
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@article{gorgolewski_,
  title = {A Practical Guide to Practicing Open Science},
  author = {Gorgolewski, Krzysztof},
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}

@article{gorgolewski_2011,
  title = {Nipype: {{A Flexible}}, {{Lightweight}} and {{Extensible Neuroimaging Data Processing Framework}} in {{Python}}},
  shorttitle = {Nipype},
  author = {Gorgolewski, Krzysztof and Burns, Christopher D. and Madison, Cindee and Clark, Dav and Halchenko, Yaroslav O. and Waskom, Michael L. and Ghosh, Satrajit S.},
  year = {2011},
  journal = {Frontiers in Neuroinformatics},
  volume = {5},
  issn = {1662-5196},
  doi = {10.3389/fninf.2011.00013},
  abstract = {Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.},
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}

@article{gorgolewski_2015,
  title = {{{NeuroVault}}.Org: A Web-Based Repository for Collecting and Sharing Unthresholded Statistical Maps of the Human Brain},
  shorttitle = {{{NeuroVault}}.Org},
  author = {Gorgolewski, Krzysztof J. and Varoquaux, Gael and Rivera, Gabriel and Schwarz, Yannick and Ghosh, Satrajit S. and Maumet, Camille and Sochat, Vanessa V. and Nichols, Thomas E. and Poldrack, Russell A. and Poline, Jean-Baptiste and Yarkoni, Tal and Margulies, Daniel S.},
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  month = apr,
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}

@article{gorgolewski_2016,
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@article{gorgolewski_2016a,
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@article{gorgolewski_2016b,
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  title = {The Brain Imaging Data Structure, a Format for Organizing and Describing Outputs of Neuroimaging Experiments},
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  abstract = {We collectedvisual event-related potentials (ERPs) from 6 normal subjects using an "oddball" paradigm. Subjects were required to count the occurrences of matching shapes presented in the left and right visualfield.Shapesmatched on 20070of trials. ERPs wererecorded from 20 or 43 electrodesdistributed over the scalp. A multiple spatio-temporal equivalent dipole (ED) model was used to fit the early sensory and P300 component. A latency window to analyze the P300 was determined using the global fieldpower statistic. The spatial topography of the P300 over this window was characterized by a midline positivity that decreased in amplitude with spatial distance from the peak. After sensory components were fit, the source of P300 could be accounted for by 1 or 2 EDs, which were usually located near medial temporal areas. This result is at odds with evidencefrom depth recordings during the oddball paradigm, showing that multiple regions of the brain are active during this interval.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NI3NVUWE\\Goto et al. - 1996 - Dipole-modeling of the visual evoked P300.pdf}
}

@article{gottlieb_2014,
  title = {Attention, {{Reward}}, and {{Information Seeking}}},
  author = {Gottlieb, J. and Hayhoe, M. and Hikosaka, O. and Rangel, A.},
  year = {2014},
  month = nov,
  journal = {Journal of Neuroscience},
  volume = {34},
  number = {46},
  pages = {15497--15504},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3270-14.2014},
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}

@article{gotz_2011,
  title = {Task Requirements Change Signal Strength of the Primary Somatosensory {{M50}}: {{Oddball}} vs. One-Back Tasks: {{Task}} Requirements Affect the {{SEF M50}}},
  shorttitle = {Task Requirements Change Signal Strength of the Primary Somatosensory {{M50}}},
  author = {G{\"o}tz, Theresa and Huonker, Ralph and Miltner, Wolfgang H. R. and Witte, Otto W. and Dettner, Konrad and Weiss, Thomas},
  year = {2011},
  month = apr,
  journal = {Psychophysiology},
  volume = {48},
  number = {4},
  pages = {569--577},
  issn = {00485772},
  doi = {10.1111/j.1469-8986.2010.01116.x},
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}

@article{grainger_1996,
  title = {Orthographic Processing in Visual Word Recognition: {{A}} Multiple Read-out Model.},
  shorttitle = {Orthographic Processing in Visual Word Recognition},
  author = {Grainger, Jonathan and Jacobs, Arthur M.},
  year = {1996},
  journal = {Psychological Review},
  volume = {103},
  number = {3},
  pages = {518--565},
  issn = {0033-295X},
  doi = {10.1037//0033-295X.103.3.518},
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  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JQUMYNFG\\Grainger and Jacobs - 1996 - Orthographic processing in visual word recognition.pdf}
}

@book{gratzer_2007,
  title = {More Math into {{LATEX}}},
  author = {Gr{\"a}tzer, George},
  year = {2007},
  edition = {4. ed},
  publisher = {{Springer}},
  address = {{New York, NY}},
  isbn = {978-0-387-68852-7 978-0-387-32289-6},
  langid = {english},
  annotation = {OCLC: 255642449},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5PJECJIC\\Grätzer_2007_More math into LATEX.pdf}
}

@article{grave_,
  title = {Imaging the {{Electrical Activity}} of the {{Brain}}: {{ELECTRA}}},
  author = {Grave, R and Menendez, Peralta and Andino, S L Gonzalez and Morand, S and Michel, C M and Landis, T},
  pages = {12},
  abstract = {The construction of a tomography of neuronal sources is limited by a lack of information. A possible way around this problem is to change the biophysical model that underlies the statement of the inverse problem, i.e., searching for magnitudes that can be better determined from the available data. In this report, we describe a mathematical characterization of the type of currents that are actually able to produce the scalp-recorded EEG. Considering this characterization, we reformulate the bioelectric inverse problem. This approach, called ELECTRA, yields some advantages over the classical formulation in terms of the current density vector: (1) the number of unknowns can be reduced, which is equivalent to increasing the number of independent measurements, (2) the constraints used to reformulate the problem are undeniable since they do not imply any hypothesis about brain function but are instead based on the character of the measurements, and (3) existing experimental evidence suggests that the proposed source model characterizes the type of currents that arise in excitable tissues. We conclude that if the latter fact proves to be true for brain tissues, then no additional information is added to the inverse problem by using a more general source model than the one proposed here. Images obtained using this method for synthetic data, as well as early and middle components of human visual evoked responses to checkerboard stimuli, are presented to illustrate the characteristics of the reconstructed maps and their interpretation. Hum. Brain Mapping 9:1\textendash 12, 2000. ௠ 2000 Wiley-Liss, Inc.},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\IFPJNMK8\\Grave et al. - Imaging the Electrical Activity of the Brain ELEC.pdf}
}

@article{gravedeperaltamenendez_2004,
  title = {Electrical Neuroimaging Based on Biophysical Constraints},
  author = {{Grave de Peralta Menendez}, Rolando and Murray, Micah M. and Michel, Christoph M. and Martuzzi, Roberto and Gonzalez Andino, Sara L.},
  year = {2004},
  month = feb,
  journal = {NeuroImage},
  volume = {21},
  number = {2},
  pages = {527--539},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2003.09.051},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LKNWUEDD\\Grave de Peralta Menendez et al_2004_NeuroImage_Electrical neuroimaging based on biophysical constraints.pdf}
}

@article{grech_2008,
  title = {Review on Solving the Inverse Problem in {{EEG}} Source Analysis},
  author = {Grech, Roberta and Cassar, Tracey and Muscat, Joseph and Camilleri, Kenneth P and Fabri, Simon G and Zervakis, Michalis and Xanthopoulos, Petros and Sakkalis, Vangelis and Vanrumste, Bart},
  year = {2008},
  journal = {Journal of NeuroEngineering and Rehabilitation},
  volume = {5},
  number = {1},
  pages = {25},
  issn = {1743-0003},
  doi = {10.1186/1743-0003-5-25},
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}

@article{green_1984,
  title = {Iteratively {{Reweighted Least Squares}} for {{Maximum Likelihood Estimation}}, and {{Some Robust}} and {{Resistant Alternatives}}},
  author = {Green, P. J.},
  year = {1984},
  month = jan,
  journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
  volume = {46},
  number = {2},
  pages = {149--170},
  issn = {00359246},
  doi = {10.1111/j.2517-6161.1984.tb01288.x},
  abstract = {The scope of applicationof iterativelryeweightedleast squaresto statisticaelstimationproblemsis considerablywiderthan is generallyappreciatedI.t extends beyondthe exponential-family-tgyepneeralizedlinearmodelsto otherdistributions, to non-lineaprarameterizationasn,d to dependentobservationsV.ariouscriterifaor estimationotherthanmaximumlikelihoodi,ncludingresistanatlternativems, aybe used.The algorithmasregenerallynumericallsytable,easilyprogrammewdithouthe aidofpackagesa,ndhighlysuitedto interactivceomputation.},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\HMCAN4E5\\Green - 1984 - Iteratively Reweighted Least Squares for Maximum L.pdf}
}

@article{green_2012,
  title = {Changes in {{Neural Connectivity Underlie Decision Threshold Modulation}} for {{Reward Maximization}}},
  author = {Green, N. and Biele, G. P. and Heekeren, H. R.},
  year = {2012},
  month = oct,
  journal = {Journal of Neuroscience},
  volume = {32},
  number = {43},
  pages = {14942--14950},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.0573-12.2012},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\C26HJHGA\\Green et al_2012_Journal of Neuroscience_Changes in Neural Connectivity Underlie Decision Threshold Modulation for.pdf;C\:\\Users\\dirko\\Zotero\\storage\\HMQBN68D\\Green et al_2012_Journal of Neuroscience_Changes in Neural Connectivity Underlie Decision Threshold Modulation for.pdf}
}

@article{greiner_2015,
  title = {Subject Pool Recruitment Procedures: Organizing Experiments with {{ORSEE}}},
  shorttitle = {Subject Pool Recruitment Procedures},
  author = {Greiner, Ben},
  year = {2015},
  month = jul,
  journal = {Journal of the Economic Science Association},
  volume = {1},
  number = {1},
  pages = {114--125},
  issn = {2199-6776, 2199-6784},
  doi = {10.1007/s40881-015-0004-4},
  abstract = {This paper discusses aspects of recruiting subjects for economic laboratory experiments, and shows how the Online Recruitment System for Economic Experiments can help. The software package provides experimenters with a free, convenient, and very powerful tool to organize their experiments and sessions.},
  langid = {english},
  annotation = {00731},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\E5HYDKPS\\Greiner_2015_Journal of the Economic Science Association_Subject pool recruitment procedures.pdf;C\:\\Users\\dirko\\Zotero\\storage\\ANHUC3K5\\s40881-015-0004-4.html}
}

@incollection{gretton_2005,
  title = {Measuring {{Statistical Dependence}} with {{Hilbert-Schmidt Norms}}},
  booktitle = {Algorithmic {{Learning Theory}}},
  author = {Gretton, Arthur and Bousquet, Olivier and Smola, Alex and Sch{\"o}lkopf, Bernhard},
  editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Dough and Vardi, Moshe Y. and Weikum, Gerhard and Jain, Sanjay and Simon, Hans Ulrich and Tomita, Etsuji},
  year = {2005},
  volume = {3734},
  pages = {63--77},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/11564089_7},
  abstract = {We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.},
  isbn = {978-3-540-29242-5 978-3-540-31696-1},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PL5HDSGC\\Gretton et al. - 2005 - Measuring Statistical Dependence with Hilbert-Schm.pdf}
}

@article{grigorescu_2002,
  title = {Comparison of Texture Features Based on {{Gabor}} Filters},
  author = {Grigorescu, S.E. and Petkov, N. and Kruizinga, P.},
  year = {2002},
  month = oct,
  journal = {IEEE Transactions on Image Processing},
  volume = {11},
  number = {10},
  pages = {1160--1167},
  issn = {1057-7149},
  doi = {10.1109/TIP.2002.804262},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MUURLT83\\Grigorescu et al_2002_IEEE Transactions on Image Processing_Comparison of texture features based on Gabor filters.pdf}
}

@article{grimbert_2006,
  title = {Bifurcation {{Analysis}} of {{Jansen}}'s {{Neural Mass Model}}},
  author = {Grimbert, Fran{\c c}ois and Faugeras, Olivier},
  year = {2006},
  month = dec,
  journal = {Neural Computation},
  volume = {18},
  number = {12},
  pages = {3052--3068},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/neco.2006.18.12.3052},
  langid = {english},
  annotation = {00000},
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}

@article{grinband_2008,
  title = {Detection of Time-Varying Signals in Event-Related {{fMRI}} Designs},
  author = {Grinband, Jack and Wager, Tor D. and Lindquist, Martin and Ferrera, Vincent P. and Hirsch, Joy},
  year = {2008},
  month = nov,
  journal = {NeuroImage},
  volume = {43},
  number = {3},
  pages = {509--520},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.07.065},
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}

@book{groeben_1975,
  title = {{Kriterien psychologischer Forschung}},
  author = {Groeben, Norbert and Westmeyer, Hans},
  year = {1975},
  series = {{Grundfragen der Psychologie}},
  publisher = {{Juventa Verlag}},
  address = {{M\"unchen}},
  isbn = {978-3-7799-0301-7},
  langid = {german},
  lccn = {BF76.5 .G76},
  keywords = {Methodology,Psychology,Research},
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}

@article{groeneveld_1990,
  title = {A {{Note}} on {{Multiple Solutions}} in {{Multivariate Restricted Maximum Likelihood Covariance Component Estimation}}},
  author = {Groeneveld, Eildert and Kovac, Milena},
  year = {1990},
  month = aug,
  journal = {Journal of Dairy Science},
  volume = {73},
  number = {8},
  pages = {2221--2229},
  issn = {00220302},
  doi = {10.3168/jds.S0022-0302(90)78902-3},
  abstract = {Restricted maximum likelihood has gained wide acceptance for estimating variance components by animal breeders. Converged solutions from maximization algorithms are usually considered REML solutions. However, neither the expectation maximization nor the derivative-free downhill simplex algorithm guarantees convergence to the global maximum. To investigate the occurrence of multiple solutions, residual and additive genetic covariance matrice{$\cdot$}s for two traits were estimated by the derivative-free approach using the downhill simplex method and an EM algorithm for a multivariate animal model. Expectation maximization yielded three different converged solutions for the data set used, while downhill simplex identified two of the three expectation maximization solutions. Multiple solutions from both expectation maximization and downhill simplex suggest the existence of local maxima. Visualization of the log-likelihood surface however, did not support this view. Instead, points of inflection in some of the dimensions could be the cause for multiple solutions. The results prove that multiple solutions can exist, casting doubt on the merit of algorithms-like expectation maximization and downhill simplex\textasciitilde at do not guarantee global maximization. Downhill simplex was superior to expectation maximization. In terms of central processing unit time it was faster by a factor of 22 misidentifying only one solution instead of 2 as expectation maximization did.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BCSJL44B\\Groeneveld and Kovac - 1990 - A Note on Multiple Solutions in Multivariate Restr.pdf}
}

@article{groeneveld_1994,
  title = {A Reparameterization to Improve Numerical Optimization in Multivariate {{REML}} (Co)Variance Component Estimation},
  author = {Groeneveld, E},
  year = {1994},
  journal = {Genetics Selection Evolution},
  volume = {26},
  number = {6},
  pages = {537},
  issn = {1297-9686},
  doi = {10.1186/1297-9686-26-6-537},
  abstract = {Multivariate restricted maximum likelihood (REML) (co)variance component estimation using numerical optimization on the basis of Downhill-Simplex (DS) or quasiNewton (QN) procedures suffers from the problem of undefined 'covariance matrices' as are produced by the optimizers. So far, this problem has been dealt with by assigning 'bad' function values. For this procedure to work, it is implied that the information this 'bad' function value conveys is sufficient to avoid going in the same direction in the following optimization step. To a limited degree DS can cope with this situation. On the other hand QN usually breaks down if this situation occurs too frequently. This contribution analyzes the problem and proposes a reparameterization of the covariance matrices to solve it. As a result, faster converging QN optimizers can be used, as they no longer suffer from lack of robustness. Four real data sets were analyzed using a multivariate model estimating between 17 and 30 (co)variance components simultaneously. Optimizing on the Cholesky factor instead of on the (co)variance components themselves reduced the computing time by a factor of 2.5 to more than 250, when comparing the robust modified DS optimizer operating on the original covariance matrices to a QN optimizer using reparameterized covariance matrices.},
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}

@book{grolemund_2014,
  title = {Hands-on Programming with {{R}}},
  author = {Grolemund, Garrett},
  year = {2014},
  edition = {First edition},
  publisher = {{O'Reilly}},
  address = {{Sebastopol, CA}},
  isbn = {978-1-4493-5901-0},
  langid = {english},
  lccn = {QA276.45.R3 G76 2014},
  keywords = {Data processing,Handbooks; manuals; etc,Mathematical statistics,Programmeertalen,R (Computer program language),Statistiek},
  annotation = {OCLC: ocn887746093},
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}

@article{groppe_2009,
  title = {Identifying Reliable Independent Components via Split-Half Comparisons},
  author = {Groppe, David M. and Makeig, Scott and Kutas, Marta},
  year = {2009},
  month = may,
  journal = {NeuroImage},
  volume = {45},
  number = {4},
  pages = {1199--1211},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.12.038},
  abstract = {Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method's validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline.},
  langid = {english},
  annotation = {00094},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\M6HSKBMS\\Groppe et al. - 2009 - Identifying reliable independent components via sp.pdf}
}

@article{guitart-masip_2012,
  title = {Go and No-Go Learning in Reward and Punishment: {{Interactions}} between Affect and Effect},
  shorttitle = {Go and No-Go Learning in Reward and Punishment},
  author = {{Guitart-Masip}, Marc and Huys, Quentin J.M. and Fuentemilla, Lluis and Dayan, Peter and Duzel, Emrah and Dolan, Raymond J.},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {1},
  pages = {154--166},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.04.024},
  abstract = {Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\5AYEZKPT\\Guitart-Masip et al. - 2012 - Go and no-go learning in reward and punishment In.pdf}
}

@article{gunther_2010,
  title = {Neuralnet: {{Training}} of {{Neural Networks}}},
  shorttitle = {Neuralnet},
  author = {G{\"u}nther, Frauke and Fritsch, Stefan},
  year = {2010},
  journal = {The R Journal},
  volume = {2},
  number = {1},
  pages = {30},
  issn = {2073-4859},
  doi = {10.32614/RJ-2010-006},
  abstract = {Artificial neural networks are applied in many situations. neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables. Thus, neural networks are used as extensions of generalized linear models.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\L5LLDWGI\\Günther und Fritsch - 2010 - neuralnet Training of Neural Networks.pdf}
}

@article{guo_2014,
  title = {A Systematic Review of the Reporting of Sample Size Calculations and Corresponding Data Components in Observational Functional Magnetic Resonance Imaging Studies},
  author = {Guo, Qing and Thabane, Lehana and Hall, Geoffrey and McKinnon, Margaret and Goeree, Ron and Pullenayegum, Eleanor},
  year = {2014},
  month = feb,
  journal = {NeuroImage},
  volume = {86},
  pages = {172--181},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2013.08.012},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5HPBDQBM\\Guo et al. - 2014 - A systematic review of the reporting of sample siz.pdf}
}

@article{ha_2018,
  title = {World {{Models}}},
  author = {Ha, David and Schmidhuber, J{\"u}rgen},
  year = {2018},
  month = mar,
  journal = {arXiv:1803.10122 [cs, stat]},
  eprint = {1803.10122},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  doi = {10.5281/zenodo.1207631},
  abstract = {We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {00159},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SYTDHK3L\\Ha und Schmidhuber - 2018 - World Models.pdf}
}

@article{haddow_2011,
  title = {`{{Nothing}} Is Really Safe': A Focus Group Study on the Processes of Anonymizing and Sharing of Health Data for Research Purposes: `{{Nothing}} Is Really Safe'},
  shorttitle = {`{{Nothing}} Is Really Safe'},
  author = {Haddow, Gill and Bruce, Ann and Sathanandam, Shiva and Wyatt, Jeremy C.},
  year = {2011},
  month = dec,
  journal = {Journal of Evaluation in Clinical Practice},
  volume = {17},
  number = {6},
  pages = {1140--1146},
  issn = {13561294},
  doi = {10.1111/j.1365-2753.2010.01488.x},
  langid = {english},
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}

@book{hadwiger_1955,
  title = {{Altes und Neues \"uber konvexe K\"orper}},
  author = {Hadwiger, H.},
  year = {1955},
  publisher = {{Birkh\"auser Basel}},
  address = {{Basel}},
  doi = {10.1007/978-3-0348-6953-9},
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@article{haefner_2016,
  title = {Perceptual {{Decision-Making}} as {{Probabilistic Inference}} by {{Neural Sampling}}},
  author = {Haefner, Ralf~M. and Berkes, Pietro and Fiser, J{\'o}zsef},
  year = {2016},
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  journal = {Neuron},
  volume = {90},
  number = {3},
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@article{haenschel_2005,
  title = {Event-{{Related Brain Potential Correlates}} of {{Human Auditory Sensory Memory-Trace Formation}}},
  author = {Haenschel, C.},
  year = {2005},
  month = nov,
  journal = {Journal of Neuroscience},
  volume = {25},
  number = {45},
  pages = {10494--10501},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.1227-05.2005},
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@article{hager_2013,
  title = {The Statistical Theories of {{Fisher}} and of {{Neyman}} and {{Pearson}}: {{A}} Methodological Perspective},
  shorttitle = {The Statistical Theories of {{Fisher}} and of {{Neyman}} and {{Pearson}}},
  author = {Hager, Willi},
  year = {2013},
  month = apr,
  journal = {Theory \& Psychology},
  volume = {23},
  number = {2},
  pages = {251--270},
  issn = {0959-3543, 1461-7447},
  doi = {10.1177/0959354312465483},
  abstract = {Most of the debates around statistical testing suffer from a failure to identify clearly the features specific to the theories invented by Fisher and by Neyman and Pearson. These features are outlined. The hybrids of Fisher's and Neyman\textendash Pearson's theory are briefly addressed. The lack of random sampling and its consequences for statistical inference are also highlighted, leading to the recommendation to dispense with inferences and perform approximate randomization tests instead. A possible scheme for the appraisal of substantive hypotheses is offered, the corroboration of which is a necessary prerequisite for scientific explanations and predictions. The scheme is partly based on the Neyman\textendash Pearson theory. This theory, though not perfect, is superior to its competitors, especially when examining substantive hypotheses. The many statistical and extra-statistical decisions prior to experimentation and the inevitable subjectivity of our research endeavors are emphasized. If feasible, statistical problems should be discussed from an extra-statistical methodological/epistemological viewpoint.},
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@article{hagler_2009,
  title = {Source Estimates for {{MEG}}/{{EEG}} Visual Evoked Responses Constrained by Multiple, Retinotopically-Mapped Stimulus Locations},
  author = {Hagler, Donald J. and Halgren, Eric and Martinez, Antigona and Huang, Mingxiong and Hillyard, Steven A. and Dale, Anders M.},
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@article{halchenko_,
  title = {Open {{Brain Consent Documentation}}},
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}

@article{halchenko_2021,
  title = {{{DataLad}}: Distributed System for Joint Management of Code, Data, and Their Relationship},
  shorttitle = {{{DataLad}}},
  author = {Halchenko, Yaroslav and Meyer, Kyle and Poldrack, Benjamin and Solanky, Debanjum and Wagner, Adina and Gors, Jason and MacFarlane, Dave and Pustina, Dorian and Sochat, Vanessa and Ghosh, Satrajit and M{\"o}nch, Christian and Markiewicz, Christopher and Waite, Laura and Shlyakhter, Ilya and {de la Vega}, Alejandro and Hayashi, Soichi and H{\"a}usler, Christian and Poline, Jean-Baptiste and Kadelka, Tobias and Skyt{\'e}n, Kusti and Jarecka, Dorota and Kennedy, David and Strauss, Ted and Cieslak, Matt and Vavra, Peter and Ioanas, Horea-Ioan and Schneider, Robin and Pfl{\"u}ger, Mika and Haxby, James and Eickhoff, Simon and Hanke, Michael},
  year = {2021},
  month = jul,
  journal = {Journal of Open Source Software},
  volume = {6},
  number = {63},
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@article{halgren_1998,
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  abstract = {Recordings directly within the brain can establish local evoked potential generation without the ambiguities always associated with extracranial electromagnetic measures. Depth recordings have found that sensory stimuli activate primary cortex and then material-specific encoders. Sensory-specific areas remain active for long periods, but by about 200 ms are joined by activation in widespread brain systems. One system is related to the orientation of attention. It is centered in paralimbic and attentional frontoparietocingular cortex, and associated with the P3a. A second system associated with P3b envelopes cognitive contextual integration. It engages the ventral temporofrontal eventencoding cortices (inferotemporal, perirhinal, and ventrolateral prefrontal), association cortices (superior temporal sulcal and posterior parietal), and the hippocampus. Thus, even in simple tasks, activation is widespread but concentrated in particular multilobar systems. With this information, the late cognitive potentials can be used to monitor the probable location, timing and intensity of brain activation during cognitive tasks. \textcopyright{} 1998 Elsevier Science Ireland Ltd.},
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@article{hallez_2007,
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  address = {{Princeton, N.J}},
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@article{hamilton_2008,
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@article{hampton_2006,
  ids = {hampton2006},
  title = {The {{Role}} of the {{Ventromedial Prefrontal Cortex}} in {{Abstract State-Based Inference}} during {{Decision Making}} in {{Humans}}},
  author = {Hampton, A. N. and Bossaerts, P. and O'Doherty, J. P.},
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  journal = {Journal of Neuroscience},
  volume = {26},
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  publisher = {{Soc Neuroscience}},
  issn = {0270-6474},
  doi = {10.1523/JNEUROSCI.1010-06.2006},
  abstract = {Many real-life decision-making problems incorporate higher-order structure, involving interdependencies between different stimuli, actions, and subsequent rewards. It is not known whether brain regions implicated in decision making, such as the ventromedial prefrontal cortex (vmPFC), use a stored model of the task structure to guide choice (model-based decision making) or merely learn action or state values without assuming higher-order structure as in standard reinforcement learning. To discriminate between these possibilities, we scanned human subjects with functional magnetic resonance imaging while they performed a simple decision-making task with higher-order structure, probabilistic reversal learning. We found that neural activity in a key decision-making region, the vmPFC, was more consistent with a computational model that exploits higher-order structure than with simple reinforcement learning. These results suggest that brain regions, such as the vmPFC, use an abstract model of task structure to guide behavioral choice, computations that may underlie the human capacity for complex social interactions and abstract strategizing.},
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  annotation = {00000}
}

@article{han_2006,
  title = {Zeng {{Lian Dept}}. of {{Mathematics Brigham Young University Provo}}, {{UT}} 84602 Zenglian@math.Byu.Edu},
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  abstract = {Manual debugging is expensive. And the high cost has motivated extensive research on automated fault localization in both software engineering and data mining communities. Fault localization aims at automatically locating likely fault locations, and hence assists manual debugging. A number of fault localization algorithms have been developed in recent years, which prove effective when multiple failing and passing cases are available. However, we notice what is more commonly encountered in practice is the two-sample debugging problem, where only one failing and one passing cases are available. This problem has been either overlooked or insufficiently tackled in previous studies.},
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@article{handwerker_2004,
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@article{handwerker_2007,
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@article{hanemann_1984,
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@article{hanin_2018,
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  journal = {arXiv:1710.11278 [cs, math, stat]},
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  primaryclass = {cs, math, stat},
  abstract = {This article concerns the expressive power of depth in deep feed-forward neural nets with ReLU activations. Specifically, we answer the following question: for a fixed din {$\geq$} 1, what is the minimal width w so that neural nets with ReLU activations, input dimension din, hidden layer widths at most w, and arbitrary depth can approximate any continuous, real-valued function of din variables arbitrarily well? It turns out that this minimal width is exactly equal to din + 1. That is, if all the hidden layer widths are bounded by din, then even in the infinite depth limit, ReLU nets can only express a very limited class of functions, and, on the other hand, any continuous function on the din-dimensional unit cube can be approximated to arbitrary precision by ReLU nets in which all hidden layers have width exactly din+1. Our construction in fact shows that any continuous function f : [0, 1]din \textrightarrow{} Rdout can be approximated by a net of width din + dout. We obtain quantitative depth estimates for such an approximation in terms of the modulus of continuity of f .},
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@article{hanke_2021,
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  abstract = {Abstract             Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.},
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@article{hannah_1996,
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@book{hanselman_2012,
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@book{hardle_2018,
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@article{hardoon_2004,
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@article{harrison_2011,
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}

@techreport{haslinger_,
  title = {{{TU Delft Strategic Plan Open Science}} 2020-2024: {{Research}} and {{Education}} in the {{Open Era}}},
  shorttitle = {{{TU Delft Strategic Plan Open Science}} 2020-2024},
  author = {Haslinger, I.M.},
  institution = {{Delft University of Technology}},
  doi = {10.4233/UUID:F2FAFF07-408F-4CEC-BD87-0919C9E4C26F},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9PS8VJYH\\Haslinger - TU Delft Strategic Plan Open Science 2020-2024 Re.pdf}
}

@article{hasofer_1978,
  title = {Upcrossings of {{Random Fields}}},
  author = {Hasofer, A. M.},
  year = {1978},
  month = mar,
  journal = {Advances in Applied Probability},
  volume = {10},
  pages = {14},
  issn = {00018678},
  doi = {10.2307/1427002},
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}

@article{hassabis_2017,
  title = {Neuroscience-{{Inspired Artificial Intelligence}}},
  author = {Hassabis, Demis and Kumaran, Dharshan and Summerfield, Christopher and Botvinick, Matthew},
  year = {2017},
  month = jul,
  journal = {Neuron},
  volume = {95},
  number = {2},
  pages = {245--258},
  issn = {0896-6273},
  doi = {10.1016/j.neuron.2017.06.011},
  abstract = {The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.},
  langid = {english},
  pmid = {28728020},
  keywords = {artificial intelligence,brain,cognition,learning,neural network},
  annotation = {00030},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QHUKE99S\\Hassabis et al_2017_Neuron_Neuroscience-Inspired Artificial Intelligence.pdf;C\:\\Users\\dirko\\Zotero\\storage\\CGV99NGI\\S0896-6273(17)30509-3.html}
}

@article{hasson_2019,
  ids = {hasson_emerging_2019},
  title = {Emerging {{Opportunities}} for {{Advancing Cognitive Neuroscience}}},
  author = {Hasson, Uri and Nusbaum, Howard C.},
  year = {2019},
  month = mar,
  journal = {Trends in Cognitive Sciences},
  issn = {13646613},
  doi = {10.1016/j.tics.2019.02.007},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AANE6QSK\\Hasson und Nusbaum - 2019 - Emerging Opportunities for Advancing Cognitive Neu.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TSVP29AI\\Hasson and Nusbaum - 2019 - Emerging Opportunities for Advancing Cognitive Neu.pdf}
}

@book{hastie_2001,
  title = {The {{Elements}} of {{Statistical Learning}}},
  author = {Hastie, Trevor and Friedman, Jerome and Tibshirani, Robert},
  year = {2001},
  series = {Springer {{Series}} in {{Statistics}}},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-0-387-21606-5},
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}

@book{hattenhauer_2020,
  title = {Informatik},
  author = {Hattenhauer, Rainer},
  year = {2020},
  edition = {Second},
  publisher = {{Pearson}},
  isbn = {978-3-86894-912-4}
}

@article{hauskrecht_,
  title = {Value-{{Function Approximations}} for {{Partially Observable Markov Decision Processes}}},
  author = {Hauskrecht, Milos},
  pages = {62},
  abstract = {Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price \textemdash exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on efficient approximation (heuristic) methods that attempt to alleviate the computational problem and trade off accuracy for speed. We have two objectives here. First, we survey various approximation methods, analyze their properties and relations and provide some new insights into their differences. Second, we present a number of new approximation methods and novel refinements of existing techniques. The theoretical results are supported by experiments on a problem from the agent navigation domain.},
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}

@article{havlicek_2011,
  title = {Dynamic Modeling of Neuronal Responses in {{fMRI}} Using Cubature {{Kalman}} Filtering},
  author = {Havlicek, Martin and Friston, Karl J. and Jan, Jiri and Brazdil, Milan and Calhoun, Vince D.},
  year = {2011},
  month = jun,
  journal = {NeuroImage},
  volume = {56},
  number = {4},
  pages = {2109--2128},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2011.03.005},
  langid = {english},
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}

@article{hawkins_2015,
  title = {Discriminating Evidence Accumulation from Urgency Signals in Speeded Decision Making},
  author = {Hawkins, Guy E. and Wagenmakers, Eric-Jan and Ratcliff, Roger and Brown, Scott D.},
  year = {2015},
  month = jul,
  journal = {Journal of Neurophysiology},
  volume = {114},
  number = {1},
  pages = {40--47},
  issn = {0022-3077, 1522-1598},
  doi = {10.1152/jn.00088.2015},
  langid = {english},
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}

@article{hawkins_2018,
  title = {Improving the {{Replicability}} of {{Psychological Science Through Pedagogy}}},
  author = {Hawkins, Robert X. D. and Smith, Eric N. and Au, Carolyn and Arias, Juan Miguel and Catapano, Rhia and Hermann, Eric and Keil, Martin and Lampinen, Andrew and Raposo, Sarah and Reynolds, Jesse and Salehi, Shima and Salloum, Justin and Tan, Jed and Frank, Michael C.},
  year = {2018},
  month = jan,
  journal = {Advances in Methods and Practices in Psychological Science},
  pages = {251524591774042},
  issn = {2515-2459, 2515-2467},
  doi = {10.1177/2515245917740427},
  abstract = {Replications are important to science, but who will do them? One proposal is that students can conduct replications as part of their training. As a proof of concept for this idea, here we report a series of 11 preregistered replications of findings from the 2015 volume of Psychological Science, all conducted as part of a graduate-level course. As was expected given larger, more systematic prior efforts, the replications typically yielded effects that were smaller than the original ones: The modal outcome was partial support for the original claim. This work documents the challenges facing motivated students as they attempt to replicate previously published results on a first attempt. We describe the workflow and pedagogical methods that were used in the class and discuss implications both for the adoption of this pedagogical model and for replication research more broadly.},
  langid = {english},
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}

@article{hayasaka_,
  title = {Derivation of the {{Euler Characteristic Densities}} of {{Non-Central T-}} and {{F-Random Fields}}},
  author = {Hayasaka, Satoru},
  pages = {10},
  langid = {english},
  annotation = {00004},
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}

@article{hayasaka_2003,
  title = {Validating Cluster Size Inference: Random Field and Permutation Methods},
  shorttitle = {Validating Cluster Size Inference},
  author = {Hayasaka, S},
  year = {2003},
  month = dec,
  journal = {NeuroImage},
  volume = {20},
  number = {4},
  pages = {2343--2356},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2003.08.003},
  langid = {english},
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}

@article{hayasaka_2004,
  title = {Nonstationary Cluster-Size Inference with Random Field and Permutation Methods},
  author = {Hayasaka, Satoru and Phan, K.Luan and Liberzon, Israel and Worsley, Keith J. and Nichols, Thomas E.},
  year = {2004},
  month = jun,
  journal = {NeuroImage},
  volume = {22},
  number = {2},
  pages = {676--687},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2004.01.041},
  langid = {english},
  annotation = {00584},
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}

@article{hayasaka_2007,
  title = {Power and Sample Size Calculation for Neuroimaging Studies by Non-Central Random Field Theory},
  author = {Hayasaka, Satoru and Peiffer, Ann M. and Hugenschmidt, Christina E. and Laurienti, Paul J.},
  year = {2007},
  month = sep,
  journal = {NeuroImage},
  volume = {37},
  number = {3},
  pages = {721--730},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.06.009},
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}

@book{haykin_1998,
  title = {Neural {{Networks}} - {{A Comprehensive Foundation}}},
  author = {Haykin, Simon S.},
  year = {1998},
  edition = {Second},
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}

@book{haykin_2009,
  title = {Neural Networks and Learning Machines},
  author = {Haykin, Simon S.},
  year = {2009},
  edition = {3rd ed},
  publisher = {{Prentice Hall}},
  address = {{New York}},
  isbn = {978-0-13-147139-9},
  lccn = {QA76.87 .H39 2009},
  keywords = {Adaptive filters,Neural networks (Computer science)},
  annotation = {04435  OCLC: ocn237325326},
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}

@article{haynes_2005,
  title = {Predicting the Orientation of Invisible Stimuli from Activity in Human Primary Visual Cortex},
  author = {Haynes, John-Dylan and Rees, Geraint},
  year = {2005},
  month = may,
  journal = {Nature Neuroscience},
  volume = {8},
  number = {5},
  pages = {686--691},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn1445},
  langid = {english},
  annotation = {00000},
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}

@article{haynes_2006,
  title = {Decoding Mental States from Brain Activity in Humans},
  author = {Haynes, John-Dylan and Rees, Geraint},
  year = {2006},
  month = jul,
  journal = {Nature Reviews Neuroscience},
  volume = {7},
  number = {7},
  pages = {523--534},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/nrn1931},
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}

@article{haynes_2015,
  title = {A {{Primer}} on {{Pattern-Based Approaches}} to {{fMRI}}: {{Principles}}, {{Pitfalls}}, and {{Perspectives}}},
  shorttitle = {A {{Primer}} on {{Pattern-Based Approaches}} to {{fMRI}}},
  author = {Haynes, John-Dylan},
  year = {2015},
  month = jul,
  journal = {Neuron},
  volume = {87},
  number = {2},
  pages = {257--270},
  issn = {08966273},
  doi = {10.1016/j.neuron.2015.05.025},
  langid = {english},
  annotation = {00000},
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}

@book{hays_1994,
  title = {Statistics},
  author = {Hays, William},
  year = {1994},
  edition = {Fifth},
  publisher = {{Harcourt Brace College Publishers}},
  annotation = {03015}
}

@article{he_2010,
  title = {Graph Theoretical Modeling of Brain Connectivity:},
  shorttitle = {Graph Theoretical Modeling of Brain Connectivity},
  author = {He, Yong and Evans, Alan},
  year = {2010},
  month = jun,
  journal = {Current Opinion in Neurology},
  pages = {1},
  issn = {1350-7540},
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}

@article{heald_2018,
  title = {Multiple Motor Memories Are Learned to Control Different Points on a Tool},
  author = {Heald, James B. and Ingram, James N. and Flanagan, J. Randall and Wolpert, Daniel M.},
  year = {2018},
  month = apr,
  journal = {Nature Human Behaviour},
  volume = {2},
  number = {4},
  pages = {300--311},
  issn = {2397-3374},
  doi = {10.1038/s41562-018-0324-5},
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}

@book{healy_2018,
  title = {Data Visualization: A Practical Introduction},
  shorttitle = {Data Visualization},
  author = {Healy, Kieran},
  year = {2018},
  publisher = {{Princeton University Press}},
  address = {{Princeton, NJ}},
  isbn = {978-0-691-18161-5 978-0-691-18162-2},
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}

@article{hearst_1997,
  title = {Banter on {{Bayes}}: Debating the Usefulness of {{Bayesian}} Approaches to Solving Practical Problems},
  shorttitle = {Banter on {{Bayes}}},
  author = {Hearst, Marti A.},
  year = {1997},
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}

@article{heckman_1979,
  title = {Sample {{Selection Bias}} as a {{Specification Error}}},
  author = {Heckman, James J.},
  year = {1979},
  month = jan,
  journal = {Econometrica},
  volume = {47},
  number = {1},
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}

@book{hedderich_2020,
  title = {{Angewandte Statistik: Methodensammlung mit R}},
  shorttitle = {{Angewandte Statistik}},
  author = {Hedderich, J{\"u}rgen and Sachs, Lothar},
  year = {2020},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-662-62294-0},
  isbn = {978-3-662-62293-3 978-3-662-62294-0},
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}

@article{hedges_,
  title = {A {{Random Effects Model}} for {{Effect Sizes}}},
  author = {Hedges, Larry V},
  pages = {8},
  langid = {english},
  annotation = {00294},
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}

@article{hedges_1981,
  title = {Distribution {{Theory}} for {{Glass}}'s {{Estimator}} of {{Effect Size}} and {{Related Estimators}}},
  author = {Hedges, Larry V},
  year = {1981},
  pages = {23},
  abstract = {Glass's estimator of effect size, the sample mean difference divided by the sample standard deviation, is studied in the context of an explicit statistical model. The exact distribution of Glass's estimator is obtained and the estimator is shown to have a small sample bias. The minimum variance unbiased estimator is obtained and shown to have uniformly smaller variance than Glass's (biased) estimator. Measurement error is shown to attenuate estimates of effect size and a correction is given. The effects of measurement invalidity are discussed. Expressions for weights that yield the most precise weighted estimate of effect size are also derived.},
  langid = {english},
  annotation = {03109},
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}

@book{hedges_1985,
  title = {Statistical {{Methods}} for {{Meta-Analysis}}},
  author = {Hedges, Larry V. and Olkin, Ingram},
  year = {1985},
  publisher = {{Academic Press}},
  annotation = {14759},
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}

@article{hedges_1992,
  title = {Modeling {{Publication Selection Effects}} in {{Meta-Analysis}}},
  author = {Hedges, Larry V.},
  year = {1992},
  month = may,
  journal = {Statistical Science},
  volume = {7},
  number = {2},
  pages = {246--255},
  issn = {0883-4237},
  doi = {10.1214/ss/1177011364},
  abstract = {Publication selection effects arise in meta-analysis when the effect magnitude estimates are observed in (available from) only a subset of the studies that were actually conducted and the probability that an estimate is observed is related to the size of that estimate. Such selection effects can lead to substantial bias in estimates of effect magnitude. Research on the selection process suggests that much of the selection occurs because researchers, reviewers and editors view the results of studies as more conclusive when they are more highly statistically significant. This suggests a model of the selection process that depends on effect magnitude via the p-value or significance level. A model of the selection process involving a step function relating the p-value to the probability of selection is introduced in the context of a random effects model for meta-analysis. The model permits estimation of a weight function representing selection along the mean and variance of effects. Some ideas for graphical procedures and a test for publication selection are also introduced. The method is then applied to a meta-analysis of test validity studies.},
  langid = {english},
  annotation = {00340},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\G6ZX8VFN\\Hedges - 1992 - Modeling Publication Selection Effects in Meta-Ana.pdf}
}

@article{hedges_1998,
  title = {Fixed- and {{Random-Effects Models}} in {{Meta-Analysis}}},
  author = {Hedges, Larry V and Vevea, Jack L},
  year = {1998},
  pages = {19},
  langid = {english},
  annotation = {02310},
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}

@article{hedges_a,
  title = {Clustering {{Estimates}} of {{Effect Magnitude From Independent Studies}}},
  author = {Hedges, Larry V and Olkin, Ingram},
  pages = {11},
  langid = {english},
  annotation = {00051},
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}

@article{heeger_2017,
  title = {Theory of Cortical Function},
  author = {Heeger, David J.},
  year = {2017},
  month = feb,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {114},
  number = {8},
  pages = {1773--1782},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1619788114},
  langid = {english},
  annotation = {00038},
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}

@article{heekeren_2004,
  title = {A General Mechanism for Perceptual Decision-Making in the Human Brain},
  author = {Heekeren, H. R. and Marrett, S. and Bandettini, P. A. and Ungerleider, L. G.},
  year = {2004},
  month = oct,
  journal = {Nature},
  volume = {431},
  number = {7010},
  pages = {859--862},
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  doi = {10.1038/nature02966},
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}

@article{heekeren_2006,
  title = {Involvement of Human Left Dorsolateral Prefrontal Cortex in Perceptual Decision Making Is Independent of Response Modality},
  author = {Heekeren, H. R. and Marrett, S. and Ruff, D. A. and Bandettini, P. A. and Ungerleider, L. G.},
  year = {2006},
  month = jun,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {103},
  number = {26},
  pages = {10023--10028},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0603949103},
  langid = {english},
  annotation = {00000},
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}

@article{heekeren_2008,
  title = {The Neural Systems That Mediate Human Perceptual Decision Making},
  author = {Heekeren, Hauke R. and Marrett, Sean and Ungerleider, Leslie G.},
  year = {2008},
  month = jun,
  journal = {Nature Reviews Neuroscience},
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  number = {6},
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  doi = {10.1038/nrn2374},
  langid = {english},
  annotation = {00000},
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}

@article{heinzle_2016,
  title = {A Hemodynamic Model for Layered {{BOLD}} Signals},
  author = {Heinzle, Jakob and Koopmans, Peter J. and {den Ouden}, Hanneke E.M. and Raman, Sudhir and Stephan, Klaas Enno},
  year = {2016},
  month = jan,
  journal = {NeuroImage},
  volume = {125},
  pages = {556--570},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2015.10.025},
  abstract = {High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the submillimeter scale has become feasible with recent advances in MR technology. In principle, this would enable the study of layered cortical circuits, one of the fundaments of cortical computation. However, the spatial layout of cortical blood supply may become an important confound at such high resolution. In particular, venous blood draining back to the cortical surface perpendicularly to the layered structure is expected to influence the measured responses in different layers. Here, we present an extension of a hemodynamic model commonly used for analyzing fMRI data (in dynamic causal models or biophysical network models) that accounts for such blood draining effects by coupling local hemodynamics across layers. We illustrate the properties of the model and its inversion by a series of simulations and show that it successfully captures layered fMRI data obtained during a simple visual experiment. We conclude that for future studies of the dynamics of layered neuronal circuits with high-resolution fMRI, it will be pivotal to include effects of blood draining, particularly when trying to infer on the layer-specific connections in cortex \textemdash{} a theme of key relevance for brain disorders like schizophrenia and for theories of brain function such as predictive coding.},
  langid = {english},
  annotation = {00029},
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}

@book{held_2008,
  title = {Methoden Der Statistischen {{Inferenz}}},
  author = {Held, Leonhard},
  year = {2008},
  publisher = {{Spektrum Akademischer Verlag}},
  address = {{Heidelberg}}
}

@book{held_2014,
  ids = {held_applied_2014a},
  title = {Applied {{Statistical Inference}}},
  author = {Held, Leonhard and Saban{\'e}s Bov{\'e}, Daniel},
  year = {2014},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-37887-4},
  isbn = {978-3-642-37886-7 978-3-642-37887-4},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MXAAFF9U\\Held und Sabanés Bové - 2014 - Applied Statistical Inference.pdf}
}

@article{helfinstein_2014,
  title = {Predicting Risky Choices from Brain Activity Patterns},
  author = {Helfinstein, S. M. and Schonberg, T. and Congdon, E. and Karlsgodt, K. H. and Mumford, J. A. and Sabb, F. W. and Cannon, T. D. and London, E. D. and Bilder, R. M. and Poldrack, R. A.},
  year = {2014},
  month = feb,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {111},
  number = {7},
  pages = {2470--2475},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1321728111},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FU4NHUUU\\Helfinstein et al_2014_Proceedings of the National Academy of Sciences_Predicting risky choices from brain activity patterns.pdf}
}

@book{henley_2019,
  title = {Hergenhahn's an Introduction to the History of Psychology},
  author = {Henley, Tracy B. and Hergenhahn, B. R.},
  year = {2019},
  edition = {Eighth edition},
  publisher = {{Cengage}},
  address = {{Australia}},
  isbn = {978-1-337-56415-1},
  langid = {english},
  lccn = {BF81 .H39 2019},
  keywords = {history,Psychology},
  annotation = {OCLC: on1035399318},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NQ2G2IXW\\Henley und Hergenhahn - 2019 - Hergenhahn's an introduction to the history of psy.pdf}
}

@article{henson_2003,
  title = {Electrophysiological and {{Haemodynamic Correlates}} of {{Face Perception}}, {{Recognition}} and {{Priming}}},
  author = {Henson, R.N.},
  year = {2003},
  month = jul,
  journal = {Cerebral Cortex},
  volume = {13},
  number = {7},
  pages = {793--805},
  issn = {1460-2199},
  doi = {10.1093/cercor/13.7.793},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\P5CKE7TC\\Henson - 2003 - Electrophysiological and Haemodynamic Correlates o.pdf}
}

@article{henson_2003a,
  title = {Neuroimaging Studies of Priming},
  author = {Henson, R.N.A},
  year = {2003},
  month = may,
  journal = {Progress in Neurobiology},
  volume = {70},
  number = {1},
  pages = {53--81},
  issn = {03010082},
  doi = {10.1016/S0301-0082(03)00086-8},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LW9DI2PF\\Henson_2003_Progress in Neurobiology_Neuroimaging studies of priming.pdf}
}

@article{henson_2005,
  title = {What Can {{Functional Neuroimaging Tell}} the {{Experimental Psychologist}}?},
  author = {Henson, Richard},
  year = {2005},
  month = feb,
  journal = {The Quarterly Journal of Experimental Psychology Section A},
  volume = {58},
  number = {2},
  pages = {193--233},
  issn = {0272-4987, 1464-0740},
  doi = {10.1080/02724980443000502},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3KXG5IGQ\\Henson_2005_The Quarterly Journal of Experimental Psychology Section A_What can Functional Neuroimaging Tell the Experimental Psychologist.pdf}
}

@article{henson_2010,
  title = {A {{Parametric Empirical Bayesian}} Framework for {{fMRI-constrained MEG}}/{{EEG}} Source Reconstruction},
  author = {Henson, Richard N. and Flandin, Guillaume and Friston, Karl J. and Mattout, J{\'e}r{\'e}mie},
  year = {2010},
  month = oct,
  journal = {Human Brain Mapping},
  volume = {31},
  number = {10},
  pages = {1512--1531},
  issn = {10659471},
  doi = {10.1002/hbm.20956},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GVSMPN56\\Henson et al_2010_Human Brain Mapping_A Parametric Empirical Bayesian framework for fMRI-constrained MEG-EEG source.pdf}
}

@article{henson_2011,
  title = {A {{Parametric Empirical Bayesian Framework}} for the {{EEG}}/{{MEG Inverse Problem}}: {{Generative Models}} for {{Multi-Subject}} and {{Multi-Modal Integration}}},
  shorttitle = {A {{Parametric Empirical Bayesian Framework}} for the {{EEG}}/{{MEG Inverse Problem}}},
  author = {Henson, Richard N. and Wakeman, Daniel G. and Litvak, Vladimir and Friston, Karl J.},
  year = {2011},
  journal = {Frontiers in Human Neuroscience},
  volume = {5},
  issn = {1662-5161},
  doi = {10.3389/fnhum.2011.00076},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VJ9V4SJE\\Henson et al_2011_Frontiers in Human Neuroscience_A Parametric Empirical Bayesian Framework for the EEG-MEG Inverse Problem.pdf}
}

@article{henson_2011a,
  title = {How to Discover Modules in Mind and Brain: {{The}} Curse of Nonlinearity, and Blessing of Neuroimaging. {{A}} Comment on {{Sternberg}} (2011)},
  shorttitle = {How to Discover Modules in Mind and Brain},
  author = {Henson, R. N.},
  year = {2011},
  month = jun,
  journal = {Cognitive Neuropsychology},
  volume = {28},
  number = {3-4},
  pages = {209--223},
  issn = {0264-3294, 1464-0627},
  doi = {10.1080/02643294.2011.561305},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\G872VTG5\\Henson_2011_Cognitive Neuropsychology_How to discover modules in mind and brain.pdf;C\:\\Users\\dirko\\Zotero\\storage\\IXANU28S\\Henson_2011_Cognitive Neuropsychology_How to discover modules in mind and brain.pdf}
}

@book{henze_2018,
  title = {{Stochastik f\"ur Einsteiger}},
  author = {Henze, Norbert},
  year = {2018},
  publisher = {{Springer Fachmedien Wiesbaden}},
  address = {{Wiesbaden}},
  doi = {10.1007/978-3-658-22044-0},
  isbn = {978-3-658-22043-3 978-3-658-22044-0},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8CRQH5G8\\Henze - 2018 - Stochastik für Einsteiger.pdf}
}

@book{henze_2018a,
  title = {{Stochastik f\"ur Einsteiger}},
  author = {Henze, Norbert},
  year = {2018},
  publisher = {{Springer Fachmedien Wiesbaden}},
  address = {{Wiesbaden}},
  doi = {10.1007/978-3-658-22044-0},
  isbn = {978-3-658-22043-3 978-3-658-22044-0},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WZPHZVTH\\Henze - 2018 - Stochastik für Einsteiger.pdf}
}

@article{hernandez_2010,
  title = {Decoding a {{Perceptual Decision Process}} across {{Cortex}}},
  author = {Hern{\'a}ndez, Adri{\'a}n and N{\'a}cher, Ver{\'o}nica and Luna, Rogelio and Zainos, Antonio and Lemus, Luis and Alvarez, Manuel and V{\'a}zquez, Yuriria and Camarillo, Liliana and Romo, Ranulfo},
  year = {2010},
  month = apr,
  journal = {Neuron},
  volume = {66},
  number = {2},
  pages = {300--314},
  issn = {08966273},
  doi = {10.1016/j.neuron.2010.03.031},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9FWXQAIE\\Hernández et al_2010_Neuron_Decoding a Perceptual Decision Process across Cortex.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TJE6CKSX\\Hernández et al_2010_Neuron_Decoding a Perceptual Decision Process across Cortex.pdf}
}

@article{herrmann_2008,
  title = {Simultaneous Recording of {{EEG}} and {{BOLD}} Responses: {{A}} Historical Perspective},
  shorttitle = {Simultaneous Recording of {{EEG}} and {{BOLD}} Responses},
  author = {Herrmann, Christoph S. and Debener, Stefan},
  year = {2008},
  month = mar,
  journal = {International Journal of Psychophysiology},
  volume = {67},
  number = {3},
  pages = {161--168},
  issn = {01678760},
  doi = {10.1016/j.ijpsycho.2007.06.006},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VSZAQ6YK\\Herrmann_Debener_2008_International Journal of Psychophysiology_Simultaneous recording of EEG and BOLD responses.pdf}
}

@inproceedings{hershey_2007,
  title = {Approximating the {{Kullback Leibler Divergence Between Gaussian Mixture Models}}},
  booktitle = {2007 {{IEEE International Conference}} on {{Acoustics}}, {{Speech}} and {{Signal Processing}} - {{ICASSP}} '07},
  author = {Hershey, John R. and Olsen, Peder A.},
  year = {2007},
  month = apr,
  pages = {IV-317-IV-320},
  publisher = {{IEEE}},
  address = {{Honolulu, HI}},
  doi = {10.1109/ICASSP.2007.366913},
  abstract = {The Kullback Leibler (KL) Divergence is a widely used tool in statistics and pattern recognition. The KL divergence between two Gaussian Mixture Models (GMMs) is frequently needed in the fields of speech and image recognition. Unfortunately the KL divergence between two GMMs is not analytically tractable, nor does any efficient computational algorithm exist. Some techniques cope with this problem by replacing the KL divergence with other functions that can be computed efficiently. We introduce two new methods, the variational approximation and the variational upper bound, and compare them to existing methods. We discuss seven different techniques in total and weigh the benefits of each one against the others. To conclude we evaluate the performance of each one through numerical experiments.},
  isbn = {978-1-4244-0727-9},
  langid = {english},
  annotation = {00639},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PDEVR9NM\\Hershey und Olsen - 2007 - Approximating the Kullback Leibler Divergence Betw.pdf}
}

@article{hertwig_2009,
  ids = {hertwig2009},
  title = {The Description\textendash Experience Gap in Risky Choice},
  author = {Hertwig, Ralph and Erev, Ido},
  year = {2009},
  month = dec,
  journal = {Trends in Cognitive Sciences},
  volume = {13},
  number = {12},
  pages = {517--523},
  publisher = {{Elsevier}},
  issn = {13646613},
  doi = {10.1016/j.tics.2009.09.004},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9BYAMCB3\\Hertwig_Erev_2009_Trends in Cognitive Sciences_The description–experience gap in risky choice.pdf}
}

@article{hertwig_2012,
  ids = {hertwig2012},
  title = {The Psychology and Rationality of Decisions from Experience},
  author = {Hertwig, Ralph},
  year = {2012},
  month = jul,
  journal = {Synthese},
  volume = {187},
  number = {1},
  pages = {269--292},
  issn = {0039-7857, 1573-0964},
  doi = {10.1007/s11229-011-0024-4},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\48PUWQN2\\Hertwig - 2012 - The psychology and rationality of decisions from e.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TL3MA35A\\Hertwig_2012_Synthese_The psychology and rationality of decisions from experience.pdf}
}

@article{heston_2017,
  title = {Predictive Power of Statistical Significance},
  author = {Heston, Thomas F and King, Jackson M},
  year = {2017},
  month = dec,
  journal = {World Journal of Methodology},
  volume = {7},
  number = {4},
  pages = {112--116},
  issn = {2222-0682},
  doi = {10.5662/wjm.v7.i4.112},
  abstract = {A statistically significant research finding should not be defined as a P -value of 0.05 or less, because this definition does not take into account study power. Statistical significance was originally defined by Fisher RA as a P -value of 0.05 or less. According to Fisher, any finding that is likely to occur by random variation no more than 1 in 20 times is considered significant. Neyman J and Pearson ES subsequently argued that Fisher's definition was incomplete. They proposed that statistical significance could only be determined by analyzing the chance of incorrectly considering a study finding was significant (a Type\,I\,error) or incorrectly considering a study finding was insignificant (a Type II error). Their definition of statistical significance is also incomplete because the error rates are considered separately, not together. A better definition of statistical significance is the positive predictive value of a P -value, which is equal to the power divided by the sum of power and the P -value. This definition is more complete and relevant than Fisher's or Neyman-Peason's definitions, because it takes into account both concepts of statistical significance. Using this definition, a statistically significant finding requires a P -value of 0.05 or less when the power is at least 95\%, and a P -value of 0.032 or less when the power is 60\%. To achieve statistical significance, P -values must be adjusted downward as the study power decreases.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\IISNTUFF\\Heston und King - 2017 - Predictive power of statistical significance.pdf}
}

@techreport{hiebeler_2010,
  title = {Matlab {{R Reference}}},
  author = {Hiebeler, David},
  year = {2010},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NKH3PDM4\\Hiebeler - 2010 - Matlab R Reference.pdf}
}

@article{higham._2001,
  title = {An {{Algorithmic Introduction}} to {{Numerical Simulation}} of {{Stochastic Differential Equations}}},
  author = {Higham., Desmond J.},
  year = {2001},
  month = jan,
  journal = {SIAM Review},
  volume = {43},
  number = {3},
  pages = {525--546},
  issn = {0036-1445, 1095-7200},
  doi = {10.1137/S0036144500378302},
  abstract = {A practical and accessible introduction to numerical methods for stochastic differential equations is given. The reader is assumed to be familiar with Euler's method for deterministic differential equations and to have at least an intuitive feel for the concept of a random variable; however, no knowledge of advanced probability theory or stochastic processes is assumed. The article is built around 10 MATLAB programs, and the topics covered include stochastic integration, the Euler\textendash Maruyama method, Milstein's method, strong and weak convergence, linear stability, and the stochastic chain rule.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KFEZ5PNA\\Higham. - 2001 - An Algorithmic Introduction to Numerical Simulatio.pdf}
}

@article{hiller_2017,
  title = {Mutualism in Museums: {{A}} Model for Engaging Undergraduates in Biodiversity Science},
  shorttitle = {Mutualism in Museums},
  author = {Hiller, Anna E. and Cicero, Carla and Albe, Monica J. and Barclay, Theresa L. W. and Spencer, Carol L. and Koo, Michelle S. and Bowie, Rauri C. K. and Lacey, Eileen A.},
  year = {2017},
  month = nov,
  journal = {PLOS Biology},
  volume = {15},
  number = {11},
  pages = {e2003318},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.2003318},
  abstract = {Museums have an untapped potential to engage students in hands-on learning. Here, we describe the development of a tiered museum-based program at the University of California, Berkeley as a model for engaging undergraduates in biodiversity science. This decade-long effort to increase student participation in collections demonstrates the mutual benefits of undergraduate involvement. Museums benefit from critical help in collections care and an increased intellectual vitality, while students simultaneously gain essential research skills and an unparalleled exposure to biodiversity. Five first steps to creating a program are: dedicate a coordinator, offer credit, diversify participation, create a tiered structure, and build community.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\I4TWAXYH\\Hiller et al. - 2017 - Mutualism in museums A model for engaging undergr.pdf;C\:\\Users\\dirko\\Zotero\\storage\\N3AN6WTT\\Hiller et al. - 2017 - Mutualism in museums A model for engaging undergr.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PYHGD63A\\Hiller et al. - 2017 - Mutualism in museums A model for engaging undergr.pdf}
}

@article{hinrichs_2000,
  title = {Deconvolution of {{Event-Related fMRI Responses}} in {{Fast-Rate Experimental Designs}}: {{Tracking Amplitude Variations}}},
  shorttitle = {Deconvolution of {{Event-Related fMRI Responses}} in {{Fast-Rate Experimental Designs}}},
  author = {Hinrichs, Hermann and Scholz, Michael and Tempelmann, Claus and Woldorff, Martin G. and Dale, Anders M. and Heinze, Hans-Jochen},
  year = {2000},
  month = nov,
  journal = {Journal of Cognitive Neuroscience},
  volume = {12},
  number = {supplement 2},
  pages = {76--89},
  issn = {0898-929X, 1530-8898},
  doi = {10.1162/089892900564082},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PQV3Q8FQ\\Hinrichs et al. - 2000 - Deconvolution of Event-Related fMRI Responses in F.pdf}
}

@article{hinrichs_2006,
  title = {Causal Visual Interactions as Revealed by an Information Theoretic Measure and {{fMRI}}},
  author = {Hinrichs, H. and Heinze, H.J. and Schoenfeld, M.A.},
  year = {2006},
  month = jul,
  journal = {NeuroImage},
  volume = {31},
  number = {3},
  pages = {1051--1060},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2006.01.038},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\X4RZZJNI\\Hinrichs et al_2006_NeuroImage_Causal visual interactions as revealed by an information theoretic measure and.pdf}
}

@article{hinton_1997,
  title = {Generative Models for Discovering Sparse Distributed Representations},
  author = {Hinton, Geoffrey E. and Ghahramani, Zoubin},
  editor = {Anderson, J. and Barlow, H. B. and Gregory, R. L.},
  year = {1997},
  month = aug,
  journal = {Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences},
  volume = {352},
  number = {1358},
  pages = {1177--1190},
  issn = {0962-8436, 1471-2970},
  doi = {10.1098/rstb.1997.0101},
  abstract = {We describe a hierarchical, generative model that can be viewed as a non-linear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KWPDBS8U\\Hinton und Ghahramani - 1997 - Generative models for discovering sparse distribut.pdf}
}

@article{hipp_2012,
  title = {Large-Scale Cortical Correlation Structure of Spontaneous Oscillatory Activity},
  author = {Hipp, Joerg F and Hawellek, David J and Corbetta, Maurizio and Siegel, Markus and Engel, Andreas K},
  year = {2012},
  month = jun,
  journal = {Nature Neuroscience},
  volume = {15},
  number = {6},
  pages = {884--890},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.3101},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\72W2DF2P\\Hipp et al_2012_Nature Neuroscience_Large-scale cortical correlation structure of spontaneous oscillatory activity.pdf;C\:\\Users\\dirko\\Zotero\\storage\\MQCKL7XZ\\Hipp et al_2012_Nature Neuroscience_Large-scale cortical correlation structure of spontaneous oscillatory activity.pdf}
}

@book{hirsch_2004,
  title = {Differential Equations, Dynamical Systems, and an Introduction to Chaos},
  author = {Hirsch, Morris W. and Smale, Stephen and Devaney, Robert L. and Hirsch, Morris W.},
  year = {2004},
  series = {Pure and Applied Mathematics; a Series of Monographs and Textbooks},
  edition = {2nd ed},
  number = {v. 60},
  publisher = {{Academic Press}},
  address = {{San Diego, CA}},
  isbn = {978-0-12-349703-1},
  lccn = {QA372 .H67 2004},
  keywords = {Algebras; Linear,Chaotic behavior in systems,Differential equations},
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}

@article{ho_2009,
  title = {Domain {{General Mechanisms}} of {{Perceptual Decision Making}} in {{Human Cortex}}},
  author = {Ho, T. C. and Brown, S. and Serences, J. T.},
  year = {2009},
  month = jul,
  journal = {Journal of Neuroscience},
  volume = {29},
  number = {27},
  pages = {8675--8687},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.5984-08.2009},
  langid = {english},
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}

@book{hochberg_1987,
  title = {Multiple Comparison Procedures},
  author = {Hochberg, Yosef and Tamhane, Ajit C.},
  year = {1987},
  series = {Wiley Series in Probability and Mathematical Statistics},
  publisher = {{Wiley}},
  address = {{New York}},
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  lccn = {QA278.4 .H63 1987},
  keywords = {Multiple comparisons (Statistics)},
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}

@book{hocking_2003,
  title = {Methods and {{Applications}} of {{Linear Models}} - {{Regression}} and the {{Analysis}} of {{Variance}}},
  author = {Hocking, Ronald},
  year = {2003},
  publisher = {{Wiley}},
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}

@article{hoeting_,
  title = {Bayesian {{Model Averaging}}: {{A Tutorial}}},
  author = {Hoeting, Jennifer A and Madigan, David and Raftery, Adrian E and Volinsky, Chris T},
  pages = {36},
  abstract = {Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples. In these examples, BMA provides improved out-ofsample predictive performance. We also provide a catalogue of currently available BMA software.},
  annotation = {00000},
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}

@article{hoffman_2013,
  ids = {hoffman_stochastic_a},
  title = {Stochastic {{Variational Inference}}},
  author = {Hoffman, Matthew D},
  year = {2013},
  pages = {45},
  abstract = {We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.},
  langid = {english},
  annotation = {01441},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2T9F8TBN\\Hoffman - Stochastic Variational Inference.pdf;C\:\\Users\\dirko\\Zotero\\storage\\AGVYGWGV\\Hoffman - Stochastic Variational Inference.pdf}
}

@book{hoffmann_2006,
  title = {Parallel Algorithms and Cluster Computing: Implementations, Algorithms and Applications},
  shorttitle = {Parallel Algorithms and Cluster Computing},
  editor = {Hoffmann, K. H. and Meyer, Arnd},
  year = {2006},
  series = {Lecture Notes in Computational Science and Engineering},
  number = {52},
  publisher = {{Springer}},
  address = {{Berlin ; New York}},
  isbn = {978-3-540-33539-9},
  langid = {english},
  lccn = {QA76.58 .P358 2006},
  keywords = {Parallel processing (Electronic computers)},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MTMNPU9Y\\Hoffmann und Meyer - 2006 - Parallel algorithms and cluster computing impleme.pdf}
}

@article{hofmann_2010,
  title = {Evaluative Conditioning in Humans: {{A}} Meta-Analysis.},
  shorttitle = {Evaluative Conditioning in Humans},
  author = {Hofmann, Wilhelm and De Houwer, Jan and Perugini, Marco and Baeyens, Frank and Crombez, Geert},
  year = {2010},
  month = may,
  journal = {Psychological Bulletin},
  volume = {136},
  number = {3},
  pages = {390--421},
  issn = {1939-1455, 0033-2909},
  doi = {10.1037/a0018916},
  abstract = {This article presents a meta-analysis of research on evaluative conditioning (EC), defined as a change in the liking of a stimulus (conditioned stimulus; CS) that results from pairing that stimulus with other positive or negative stimuli (unconditioned stimulus; US). Across a total of 214 studies included in the main sample, the mean EC effect was d ϭ .52, with a 95\% confidence interval of .466 \textendash.582. As estimated from a random-effects model, about 70\% of the variance in effect sizes were attributable to true systematic variation rather than sampling error. Moderator analyses were conducted to partially explain this variation, both as a function of concrete aspects of the procedural implementation and as a function of the abstract aspects of the relation between CS and US. Among a range of other findings, EC effects were stronger for high than for low contingency awareness, for supraliminal than for subliminal US presentation, for postacquisition than for postextinction effects, and for self-report than for implicit measures. These findings are discussed with regard to the procedural boundary conditions of EC and theoretical accounts about the mental processes underlying EC.},
  langid = {english},
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}

@article{hogben_1961,
  title = {The {{Moments}} of the {{Non-Central}} t-{{Distribution}}},
  author = {Hogben, D. and Pinkham, R.S and Wilk, M.B.},
  year = {1961},
  pages = {5},
  abstract = {The problem of estimatirng missing split-plots when entire whole-plots are missing is discussed. The simultaneous equations for estimating the missing values are singular and inconsistent, but they can be solved subject to the condition that the sum of the unknowns equals the value which minimizes the wholeplot error mean squiare. If some of the values are merely mixed-up a direct solution is always available.},
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}

@article{hohwy_2012,
  title = {Attention and {{Conscious Perception}} in the {{Hypothesis Testing Brain}}},
  author = {Hohwy, Jakob},
  year = {2012},
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}

@article{holmes_,
  title = {Generalisability, {{Random E}} Ects \& {{Population Inference}}},
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@article{holmes_2003,
  title = {The Processing of Emotional Facial Expression Is Gated by Spatial Attention: Evidence from Event-Related Brain Potentials},
  shorttitle = {The Processing of Emotional Facial Expression Is Gated by Spatial Attention},
  author = {Holmes, Amanda and Vuilleumier, Patrik and Eimer, Martin},
  year = {2003},
  month = apr,
  journal = {Cognitive Brain Research},
  volume = {16},
  number = {2},
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  doi = {10.1016/S0926-6410(02)00268-9},
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}

@article{holroyd_2002,
  title = {The Neural Basis of Human Error Processing: {{Reinforcement}} Learning, Dopamine, and the Error-Related Negativity.},
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  author = {Holroyd, Clay B. and Coles, Michael G.H.},
  year = {2002},
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  number = {4},
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}

@book{holzinger_1939,
  title = {A Study in Factor Analysis: {{The}} Stability of a Bifactor Solution},
  author = {Holzinger, K. J. and Swineford, F.},
  year = {1939},
  series = {Supplementary {{Educational Monographs}}},
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}

@article{honert_2017,
  title = {Challenges for {{Bayesian Model Selection}} of {{Dynamic Causal Models}}},
  author = {van den Honert, Rebecca and Shultz, Sarah and Johnson, Marcia K. and McCarthy, Gregory},
  year = {2017},
  month = jan,
  journal = {bioRxiv},
  pages = {102293},
  doi = {10.1101/102293},
  abstract = {Achieving a mechanistic explanation of brain function requires understanding causal relationships among regions. A relatively new technique to assess effective connectivity in fMRI data is Dynamic Causal Modeling (DCM). As DCM is more frequently used, it becomes increasingly important to further validate the technique and understand its limitations. With DCM, Bayesian Model Selection (BMS) is used to select the most likely causal model. We conducted simulations to test the degree to which BMS is robust to two types of challenges when applied to DCMs, those inherent to data (Category 1) and those inherent to model space (Category 2). Category 1 challenges tested properties of the data (low signal-to-noise, different response magnitudes and shapes across regions) that could either blur the distinction between models or potentially bias model selection. These challenges are impossible or difficult to measure and control in real data, so investigating their effect upon BMS through simulation is critical. Category 2 challenges tested properties of model space that create subsets of confusable models. Our results suggest that given data that conform to the prior assumptions of DCM, BMS is robust to challenges from Category 1. However, in the face of Category 2 challenges (when a more homogenous model space was tested) the false positive rate rose above an acceptable level. We show that such errors are neither trivial nor easily avoided with existing approaches. However, we argue that it is possible to detect Category 2 challenges, and avoid inappropriate interpretations by conducting simulations prior to applying DCM.},
  copyright = {\textcopyright{} 2017, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
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@article{hooper_1959,
  title = {Simultaneous {{Equations}} and {{Canonical Correlation Theory}}},
  author = {Hooper, John W.},
  year = {1959},
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  journal = {Econometrica},
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  journal = {Proceedings of the National Academy of Sciences},
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@article{hopfield_2000,
  title = {What Is a Moment? "{{Cortical}}" Sensory Integration over a Brief Interval},
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}

@article{hopfield_2001,
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  author = {Hopfield, J. J.},
  year = {2001},
  month = jan,
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@article{hopfinger_,
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@book{horn_2012,
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  author = {Horn, Roger A. and Johnson, Charles R.},
  year = {2012},
  edition = {2nd ed},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge ; New York}},
  abstract = {"The thoroughly revised and updated second edition of this acclaimed text has several new and expanded sections and more than 1,100 exercises"--},
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@article{horn_2014,
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  author = {Horn, Andreas and Ostwald, Dirk and Reisert, Marco and Blankenburg, Felix},
  year = {2014},
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  journal = {NeuroImage},
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}

@article{hornik_1989,
  title = {Multilayer Feedforward Networks Are Universal Approximators},
  author = {Hornik, Kurt and Stinchcombe, Maxwell and White, Halbert},
  year = {1989},
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  journal = {Neural Networks},
  volume = {2},
  number = {5},
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  doi = {10.1016/0893-6080(89)90020-8},
  abstract = {This paper rigorously establishes thut standard rnultiluyer feedforward networks with as f\&v us one hidden layer using arbitrary squashing functions ure capable of upproximating uny Bore1 measurable function from one finite dimensional space to another to any desired degree of uccuracy, provided sujficirntly muny hidden units are available. In this sense, multilayer feedforward networks are u class of universul rlpproximators.},
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  annotation = {19501},
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}

@article{hornik_1991,
  title = {Approximation Capabilities of Multilayer Feedforward Networks},
  author = {Hornik, Kurt},
  year = {1991},
  journal = {Neural Networks},
  volume = {4},
  number = {2},
  pages = {251--257},
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  doi = {10.1016/0893-6080(91)90009-T},
  abstract = {We show that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to V(p.) performance criteria, for arbitrary finite input environment measures Jl., provided only that sufficiently many hidden units are available. If the activation function is continuous, bounded and nonconstant, then continuous mappings can be learned uniformly over compact input sets. We also give very general conditions ensuring that networks with sufficiently smooth activation functions are capable of arbitrarily accurate approximation to a function and its derivatives.},
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}

@article{horovitz_2004,
  title = {Parametric Design and Correlational Analyses Help Integrating {{fMRI}} and Electrophysiological Data during Face Processing},
  author = {Horovitz, Silvina G and Rossion, Bruno and Skudlarski, Pawel and Gore, John C},
  year = {2004},
  month = aug,
  journal = {NeuroImage},
  volume = {22},
  number = {4},
  pages = {1587--1595},
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  doi = {10.1016/j.neuroimage.2004.04.018},
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}

@article{horvath_2021,
  title = {Human {{Belief State-Based Exploration}} and {{Exploitation}} in an {{Information-Selective Symmetric Reversal Bandit Task}}},
  author = {Horvath, Lilla and Colcombe, Stanley and Milham, Michael and Ray, Shruti and Schwartenbeck, Philipp and Ostwald, Dirk},
  year = {2021},
  month = aug,
  journal = {Computational Brain \& Behavior},
  issn = {2522-0861, 2522-087X},
  doi = {10.1007/s42113-021-00112-3},
  abstract = {Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. In the current exploratory study, we introduce an information-selective symmetric reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioral models, including exploitative and explorative Bayesian agents, as well as heuristic control agents. Upon validating the model and parameter recovery properties of our model set and summarizing the participants' choice data in a descriptive way, we used a maximum likelihood approach to evaluate the participants' choice data from the perspective of our model set. In brief, we provide quantitative evidence that participants employ a belief state-based hybrid explorative-exploitative strategy on the information-selective symmetric reversal bandit task, lending further support to the finding that humans are guided by their subjective uncertainty when solving exploration-exploitation dilemmas.},
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}

@article{horwitz_2002,
  title = {How Can {{EEG}}/{{MEG}} and {{fMRI}}/{{PET}} Data Be Combined?},
  author = {Horwitz, Barry and Poeppel, David},
  year = {2002},
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  number = {1},
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  doi = {10.1002/hbm.10057},
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}

@article{hotelling_1931,
  title = {The {{Generalization}} of {{Student}}'s {{Ratio}}},
  author = {Hotelling, Harold},
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  journal = {The Annals of Mathematical Statistics},
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}

@article{hotelling_1933,
  title = {Analysis of Complex Variables into Principal Components},
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}

@article{hotelling_1936,
  title = {Relations {{Between Two Sets}} of {{Variates}}},
  author = {Hotelling, Harold},
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@article{houthakker_1950,
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@book{hox_2010,
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  shorttitle = {Multilevel Analysis},
  author = {Hox, Joop J.},
  year = {2010},
  series = {Quantitative Methodology Series},
  edition = {2. ed},
  publisher = {{Routledge, Taylor \& Francis}},
  address = {{New York}},
  abstract = {Introduction to multilevel analysis -- The basic two-level regression model -- Estimation and hypothesis testing in multilevel regression -- Some important methodological and statistical issues -- Analyzing longitudinal data -- The multilevel generalized linear model for dichotomous data and proportions -- The multilevel generalized linear model for categorical and count data -- Multilevel survival analysis -- Cross-classified multilevel models -- Multivariate multilevel regression models -- The multilevel approach to meta-analysis -- Sample sizes and power analysis in multilevel regression -- Advanced issues in estimation and testing -- Multilevel factor models -- Multilevel path models -- Latent curve models},
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@book{hsu_1999,
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  year = {1999},
  edition = {Reprint},
  publisher = {{Chapman \& Hall/CRC}},
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@article{huang_2012,
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@article{huang_2013,
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  editor = {{de Lange}, Floris P.},
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@article{hubel_2009,
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@inproceedings{huber_2008,
  title = {On Entropy Approximation for {{Gaussian}} Mixture Random Vectors},
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  month = aug,
  pages = {181--188},
  publisher = {{IEEE}},
  doi = {10.1109/MFI.2008.4648062},
  abstract = {For many practical probability density representations such as for the widely used Gaussian mixture densities, an analytic evaluation of the differential entropy is not possible and thus, approximate calculations are inevitable. For this purpose, the first contribution of this paper deals with a novel entropy approximation method for Gaussian mixture random vectors, which is based on a component-wise Taylor-series expansion of the logarithm of a Gaussian mixture and on a splitting method of Gaussian mixture components. The employed order of the Taylor-series expansion and the number of components used for splitting allows balancing between accuracy and computational demand. The second contribution is the determination of meaningful and efficiently to calculate lower and upper bounds of the entropy, which can be also used for approximation purposes. In addition, a refinement method for the more important upper bound is proposed in order to approach the true entropy value.},
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@article{huberty_,
  title = {Applied {{MANOVA}} and {{Discriminant Analysis}}},
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}

@article{huettel_2004,
  title = {Linking {{Hemodynamic}} and {{Electrophysiological Measures}} of {{Brain Activity}}: {{Evidence}} from {{Functional MRI}} and {{Intracranial Field Potentials}}},
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  author = {Huettel, S. A.},
  year = {2004},
  month = feb,
  journal = {Cerebral Cortex},
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  number = {2},
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@book{huettel_2014,
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  author = {Huettel, Scott A. and Song, Allen W. and McCarthy, Gregory},
  year = {2014},
  edition = {3rd ed},
  publisher = {{Sinauer Associates}},
  address = {{Sunderland, Mass}},
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@article{hunt_2012,
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  journal = {Nature Neuroscience},
  volume = {15},
  number = {3},
  pages = {470--476},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.3017},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\76JIMUN8\\Hunt et al_2012_Nature Neuroscience_Mechanisms underlying cortical activity during value-guided choice.pdf}
}

@article{hunt_2019,
  title = {The Life-Changing Magic of Sharing Your Data},
  author = {Hunt, Laurence T.},
  year = {2019},
  month = apr,
  journal = {Nature Human Behaviour},
  volume = {3},
  number = {4},
  pages = {312--315},
  issn = {2397-3374},
  doi = {10.1038/s41562-019-0560-3},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5RFQ6M6L\\Hunt - 2019 - The life-changing magic of sharing your data.pdf}
}

@article{hunt_2021,
  title = {Formalizing Planning and Information Search in Naturalistic Decision-Making},
  author = {Hunt, L. T. and Daw, N. D. and Kaanders, P. and MacIver, M. A. and Mugan, U. and Procyk, E. and Redish, A. D. and Russo, E. and Scholl, J. and Stachenfeld, K. and Wilson, C. R. E. and Kolling, N.},
  year = {2021},
  month = aug,
  journal = {Nature Neuroscience},
  volume = {24},
  number = {8},
  pages = {1051--1064},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-021-00866-w},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\TUH4D8XQ\\Hunt et al. - 2021 - Formalizing planning and information search in nat.pdf}
}

@article{husedzinovic_2015,
  title = {Stakeholders' Perspectives on Biobank-Based Genomic Research: Systematic Review of the Literature},
  shorttitle = {Stakeholders' Perspectives on Biobank-Based Genomic Research},
  author = {Husedzinovic, Alma and Ose, Dominik and Schickhardt, Christoph and Fr{\"o}hling, Stefan and Winkler, Eva C},
  year = {2015},
  month = dec,
  journal = {European Journal of Human Genetics},
  volume = {23},
  number = {12},
  pages = {1607--1614},
  issn = {1018-4813, 1476-5438},
  doi = {10.1038/ejhg.2015.27},
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}

@article{hutson_2018,
  title = {Artificial Intelligence Faces Reproducibility Crisis},
  author = {Hutson, Matthew},
  year = {2018},
  month = feb,
  journal = {Science},
  volume = {359},
  number = {6377},
  pages = {725--726},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.359.6377.725},
  abstract = {The booming field of artificial intelligence (AI) is grappling with a replication crisis, much like the ones that have afflicted psychology, medicine, and other fields over the past decade. Just because algorithms are based on code doesn't mean experiments are easily replicated. Far from it. Unpublished codes and a sensitivity to training conditions have made it difficult for AI researchers to reproduce many key results. That is leading to a new conscientiousness about research methods and publication protocols. Last week, at a meeting of the Association for the Advancement of Artificial Intelligence in New Orleans, Louisiana, reproducibility was on the agenda, with some teams diagnosing the problem\textemdash and one laying out tools to mitigate it. Unpublished code and sensitivity to training conditions make many claims hard to verify. Unpublished code and sensitivity to training conditions make many claims hard to verify.},
  copyright = {Copyright \textcopyright{} 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works},
  langid = {english},
  pmid = {29449469},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7RSJ7PF9\\Hutson_2018_Science_Artificial intelligence faces reproducibility crisis.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TL7J5ULA\\725.html}
}

@article{hutzler_2014,
  title = {Reverse Inference Is Not a Fallacy per Se: {{Cognitive}} Processes Can Be Inferred from Functional Imaging Data},
  shorttitle = {Reverse Inference Is Not a Fallacy per Se},
  author = {Hutzler, Florian},
  year = {2014},
  month = jan,
  journal = {NeuroImage},
  volume = {84},
  pages = {1061--1069},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.12.075},
  langid = {english},
  annotation = {00000},
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}

@article{huys_2011,
  title = {Disentangling the {{Roles}} of {{Approach}}, {{Activation}} and {{Valence}} in {{Instrumental}} and {{Pavlovian Responding}}},
  author = {Huys, Quentin J. M. and Cools, Roshan and G{\"o}lzer, Martin and Friedel, Eva and Heinz, Andreas and Dolan, Raymond J. and Dayan, Peter},
  editor = {Rangel, Antonio},
  year = {2011},
  month = apr,
  journal = {PLoS Computational Biology},
  volume = {7},
  number = {4},
  pages = {e1002028},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1002028},
  abstract = {Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UA5RSUEX\\Huys et al. - 2011 - Disentangling the Roles of Approach, Activation an.pdf}
}

@article{hyndman_1996,
  title = {Sample {{Quantiles}} in {{Statistical Packages}}},
  author = {Hyndman, Rob J. and Fan, Yanan},
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  journal = {The American Statistician},
  volume = {50},
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@article{hyvarinen_,
  title = {The {{Fixed-Point Algorithm}} and {{Maximum Likelihood Estimation}} for {{Independent Component Analysis}}},
  author = {Hyv{\"a}rinen, Aapo},
  pages = {6},
  abstract = {The author previously introduced a fast fixed-point algorithm for independent component analysis. The algorithm was derived from objective functions motivated by projection pursuit. In this paper, it is shown that the algorithm is closely connected to maximum likelihood estimation as well. The basic fixed-point algorithm maximizes the likelihood under the constraint of decorrelation, if the score function is used as the nonlinearity. Modifications of the algorithm maximize the likelihood without constraints.},
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}

@article{hyvarinen_1999,
  title = {Fast and Robust Fixed-Point Algorithms for Independent Component Analysis},
  author = {Hyv{\"a}rinen, Aapo},
  year = {1999},
  month = may,
  journal = {IEEE Transactions on Neural Networks},
  volume = {10},
  number = {3},
  pages = {626--634},
  issn = {1045-9227, 1941-0093},
  doi = {10.1109/72.761722},
  abstract = {Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.},
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}

@article{hyvarinen_2000,
  title = {Independent Component Analysis: Algorithms and Applications},
  shorttitle = {Independent Component Analysis},
  author = {Hyv{\"a}rinen, A. and Oja, E.},
  year = {2000},
  month = jun,
  journal = {Neural Networks},
  volume = {13},
  number = {4-5},
  pages = {411--430},
  issn = {08936080},
  doi = {10.1016/S0893-6080(00)00026-5},
  abstract = {A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject. ᭧ 2000 Published by Elsevier Science Ltd.},
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  annotation = {19617},
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}

@book{hyvarinen_2001,
  title = {Independent {{Component Analysis}}},
  author = {Hyv{\"a}rinen, Aapo and Karhunen, Juha and Oja, Erkki},
  year = {2001},
  publisher = {{Wiley-Interscience}},
  address = {{S.l}},
  abstract = {A comprehensive introduction to ICA for students and practitionersIndependent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more.Independent Component Analysis is divided into four sections that cover:*General mathematical concepts utilized in the book*The basic ICA model and its solution*Various extensions of the basic ICA model*Real-world applications for ICA modelsAuthors Hyvarinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative A comprehensive introduction to ICA for students and practitionersIndependent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and},
  isbn = {978-0-471-22131-9 978-0-471-40540-5 978-0-471-46419-8},
  langid = {english},
  annotation = {19617  OCLC: 845874768},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MDHBA9QF\\Hyvärinen et al. - 2001 - Independent Component Analysis.pdf}
}

@article{iglesias_2013,
  ids = {iglesias_hierarchical_2013},
  title = {Hierarchical {{Prediction Errors}} in {{Midbrain}} and {{Basal Forebrain}} during {{Sensory Learning}}},
  author = {Iglesias, Sandra and Mathys, Christoph and Brodersen, Kay~H. and Kasper, Lars and Piccirelli, Marco and {den~Ouden}, Hanneke~E.M. and Stephan, Klaas~E.},
  year = {2013},
  month = oct,
  journal = {Neuron},
  volume = {80},
  number = {2},
  pages = {519--530},
  issn = {08966273},
  doi = {10.1016/j.neuron.2013.09.009},
  langid = {english},
  annotation = {00000},
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}

@article{ihaka_1996,
  title = {R: {{A Language}} for {{Data Analysis}} and {{Graphics}}},
  author = {Ihaka, Ross and Gentleman, Robert},
  year = {1996},
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  volume = {5},
  number = {3},
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}

@article{ihaka_2021,
  title = {R: {{A Language}} for {{Data Analysis}} and {{Graphics}}},
  author = {Ihaka, Ross and Gentleman, Robert},
  year = {2021},
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@article{iigaya_2016,
  title = {The Modulation of Savouring by Prediction Error and Its Effects on Choice},
  author = {Iigaya, Kiyohito and Story, Giles W. and {Kurth-Nelson}, Zeb and Dolan, Raymond J. and Dayan, Peter},
  year = {2016},
  month = apr,
  journal = {eLife},
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  pages = {e13747},
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  abstract = {The anticipation of rewards turns out to have its own hedonic value, on top of that of the reward itself; a wide range of behavioral and neurophysiological data suggest that this anticipation is boosted by prediction errors.},
  copyright = {\textcopyright{} 2016 Iigaya et al.. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.},
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}

@article{ilmoniemi_1993,
  title = {Models of Source Currents in the Brain},
  author = {Ilmoniemi, R. J.},
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  pages = {331--336},
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  doi = {10.1007/BF01128686},
  abstract = {Electroencephalography (EEG)and magnetoencephalography (MEG)provide signalsthat are weighted integrals ofsource currents in the brain. In addition to technical aspects, the two methods differ in their sensitivities to various cerebral sources. Moreover, it is more difficult to determine the lead fields of EEG than of MEG. If it can be assumed that only one localizedsource is active at a particular time, the source location, direction, and amplitude can be found with the dipole model. However, if the assumption of a single localizedsource is violated, erroneous results are obtained. If a few sources are responsible for the measured fields, multiple-dipole models can be used. In the general case one must start from the fundamentals of estimation theory. The use of a priori information, together with experimental data, will provide the best possible solution to the inverse problem. In the case of minimal prior information, the so-calledminimum-norm solution is obtained. With the help of supplementary information, the resolution can be further improved.},
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}

@book{imbens_2015,
  title = {Causal {{Inference}} for {{Statistics}}, {{Social}}, and {{Biomedical Sciences}}: {{An Introduction}}},
  shorttitle = {Causal {{Inference}} for {{Statistics}}, {{Social}}, and {{Biomedical Sciences}}},
  author = {Imbens, Guido and Rubin, Donald B.},
  year = {2015},
  publisher = {{Academic Press}},
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}

@article{ingre_2013,
  title = {Why Small Low-Powered Studies Are Worse than Large High-Powered Studies and How to Protect against ``Trivial'' Findings in Research: {{Comment}} on {{Friston}} (2012)},
  shorttitle = {Why Small Low-Powered Studies Are Worse than Large High-Powered Studies and How to Protect against ``Trivial'' Findings in Research},
  author = {Ingre, Michael},
  year = {2013},
  month = nov,
  journal = {NeuroImage},
  volume = {81},
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  issn = {10538119},
  doi = {10.1016/j.neuroimage.2013.03.030},
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}

@article{ioannidis_2005,
  title = {Why {{Most Published Research Findings Are False}}},
  author = {Ioannidis, John P A},
  year = {2005},
  journal = {PLoS Medicine},
  volume = {2},
  number = {8},
  pages = {6},
  abstract = {There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.},
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}

@article{isomura_2020,
  title = {Reverse-{{Engineering Neural Networks}} to {{Characterize Their Cost Functions}}},
  author = {Isomura, Takuya and Friston, Karl},
  year = {2020},
  month = nov,
  journal = {Neural Computation},
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  number = {11},
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  issn = {0899-7667, 1530-888X},
  doi = {10.1162/neco_a_01315},
  abstract = {This letter considers a class of biologically plausible cost functions for neural networks, where the same cost function is minimized by both neural activity and plasticity. We show that such cost functions can be cast as a variational bound on model evidence under an implicit generative model. Using generative models based on partially observed Markov decision processes (POMDP), we show that neural activity and plasticity perform Bayesian inference and learning, respectively, by maximizing model evidence. Using mathematical and numerical analyses, we establish the formal equivalence between neural network cost functions and variational free energy under some prior beliefs about latent states that generate inputs. These prior beliefs are determined by particular constants (e.g., thresholds) that define the cost function. This means that the Bayes optimal encoding of latent or hidden states is achieved when the network's implicit priors match the process that generates its inputs. This equivalence is potentially important because it suggests that any hyperparameter of a neural network can itself be optimized\textemdash by minimization with respect to variational free energy. Furthermore, it enables one to characterize a neural network formally, in terms of its prior beliefs.},
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@article{itthipuripat_2015,
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@inproceedings{itti_2005,
  title = {A Principled Approach to Detecting Surprising Events in Video},
  booktitle = {Computer {{Vision}} and {{Pattern Recognition}}, 2005. {{CVPR}} 2005. {{IEEE Computer Society Conference}} On},
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@inproceedings{itti_2006,
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  booktitle = {Advances in Neural Information Processing Systems},
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@book{izenman_2008,
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@book{izhikevich_2007,
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  shorttitle = {Dynamical Systems in Neuroscience},
  author = {Izhikevich, Eugene M.},
  year = {2007},
  series = {Computational Neuroscience},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-09043-8},
  lccn = {QP355.2 .I94 2007},
  keywords = {Differentiable dynamical systems,Models; Neurological,Neurology,Neurons,Neurosciences},
  annotation = {OCLC: ocm65400606},
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}

@article{j.cao_1999,
  title = {The {{Size}} of the {{Connected Components}} of {{Excursion Sets}} of {$\chi$} 2, t and {{F Fields}}},
  author = {J. Cao},
  year = {1999},
  journal = {Advances in Applied Probability},
  volume = {31},
  number = {3},
  pages = {579--595},
  abstract = {The distribution of the size of one connected component and the largest connected component of the excursion set is derived for stationary X2, t and F fields, in the limit of high or low thresholds. This extends previous results for stationary Gaussian fields (Nosko 1969, Adler 1981) and for X2 fields in one and two dimensions (Aronowich and Adler 1986, 1988). An application of this is to detect regional changes in positron emission tomography (PET) images of blood flow in human brain, using the size of the largest connected component of the excursion set as a test statistic.},
  langid = {english},
  annotation = {00129},
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}

@article{jacobs_1994,
  title = {Models of Visual Word Recognition: Sampling the State of the Art.},
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@article{jaeger_1998,
  title = {On {{Confirmatory}} versus {{Exploratory Research}}},
  author = {Jaeger, Robert G and Halliday, Tim R},
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  volume = {54},
  pages = {64--66},
  abstract = {We discuss the difference between data that are gathered to test a priori alternative hypotheses (confirmatory research) and those gathered to determine whether any interesting a posteriori hypotheses might be generated from the data set (exploratory research). Confirmatory studies should be designed so as to minimize type I statistical errors while exploratory studies should be designed to minimize type II errors, which may require different approaches to statistical analyses. We suggest that authors clearly state whether they are reporting confirmatory or exploratory research in manuscripts, especially for experimental studies, because evaluations by referees and readers often hinge on such a distinction.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\64YH453V\\Jaeger' und Halliday - 1998 - On Confirmatory versus Exploratory Research.pdf}
}

@book{james_2013,
  title = {An Introduction to Statistical Learning: With Applications in {{R}}},
  shorttitle = {An Introduction to Statistical Learning},
  editor = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  year = {2013},
  series = {Springer Texts in Statistics},
  number = {103},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-1-4614-7137-0},
  langid = {english},
  lccn = {QA276 .I585 2013},
  keywords = {Mathematical models,Mathematical statistics,Problems; exercises; etc,R (Computer program language),Statistics},
  annotation = {00036  OCLC: ocn828488009},
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}

@article{jansen_1993,
  title = {A Neurophysiologically-Based Mathematical Model of Flash Visual Evoked Potentials},
  author = {Jansen, Ben H. and Zouridakis, George and Brandt, Michael E.},
  year = {1993},
  journal = {Biological cybernetics},
  volume = {68},
  number = {3},
  pages = {275--283},
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}

@article{jansen_1995,
  title = {Electroencephalogram and Visual Evoked Potential Generation in a Mathematical Model of Coupled Cortical Columns},
  author = {Jansen, Ben H. and Rit, Vincent G.},
  year = {1995},
  month = sep,
  journal = {Biological Cybernetics},
  volume = {73},
  number = {4},
  pages = {357--366},
  issn = {0340-1200, 1432-0770},
  doi = {10.1007/s004220050191},
  abstract = {This study deals with neurophysiologically based models simulating electrical brain activity (i.e., the electroencephalogram or EEG, and evoked potentials or EPs). A previously developed lumped-parameter model of a single cortical column was implemented using a more accurate computational procedure. Anatomically acceptable values for the various model parameters were determined, and a multi-dimensional exploration of the model parameter-space was conducted. It was found that the model could produce a large variety of EEG-like waveforms and rhythms. Coupling two models, with delays in the interconnections to simulate the synaptic connections within and between cortical areas, made it possible to replicate the spatial distribution of alpha and beta activity. EPs were simulated by presenting pulses to the input of the coupled models. In general, the responses were more realistic than those produced using a single model. Our simulations also suggest that the scalp-recorded EP is at least partially due to a phase reordering of the ongoing activity.},
  annotation = {00660},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\85RZWL63\\Jansen and Rit - 1995 - Electroencephalogram and visual evoked potential g.pdf;C\:\\Users\\dirko\\Zotero\\storage\\F3SHG7FI\\Jansen and Rit - 1995 - Electroencephalogram and visual evoked potential g.pdf}
}

@article{janz_2001,
  title = {Coupling of Neural Activity and {{BOLD fMRI}} Response: {{New}} Insights by Combination of {{fMRI}} and {{VEP}} Experiments in Transition from Single Events to Continuous Stimulation},
  shorttitle = {Coupling of Neural Activity and {{BOLD fMRI}} Response},
  author = {Janz, C. and Heinrich, S.P. and Kornmayer, J. and Bach, M. and Hennig, J.},
  year = {2001},
  month = sep,
  journal = {Magnetic Resonance in Medicine},
  volume = {46},
  number = {3},
  pages = {482--486},
  issn = {0740-3194, 1522-2594},
  doi = {10.1002/mrm.1217},
  langid = {english},
  annotation = {00071},
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}

@book{jaynes_2003,
  title = {Probability Theory: The Logic of Science},
  shorttitle = {Probability Theory},
  author = {Jaynes, E. T and Bretthorst, G. Larry},
  year = {2003},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge, UK; New York, NY}},
  abstract = {A comprehensive introduction to the role of probability theory in general scientific endeavour. This book provides an original interpretation of probability theory, showing the subject to be an extension of logic, and presenting new results and applications. Ideal for scientists working in any area involving inference from incomplete information.},
  isbn = {978-0-511-06589-7 978-0-521-59271-0 978-0-511-06802-7 978-0-511-79042-3 978-1-139-63632-2 978-1-280-41722-1},
  langid = {english},
  annotation = {OCLC: 57254076},
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}

@article{jbabdi_2007,
  title = {A {{Bayesian}} Framework for Global Tractography},
  author = {Jbabdi, S. and Woolrich, M.W. and Andersson, J.L.R. and Behrens, T.E.J.},
  year = {2007},
  month = aug,
  journal = {NeuroImage},
  volume = {37},
  number = {1},
  pages = {116--129},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.04.039},
  langid = {english},
  annotation = {00000},
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}

@book{jeffreys_1998,
  title = {Theory of Probability},
  author = {Jeffreys, Harold},
  year = {1998},
  series = {Oxford Classic Texts in the Physical Sciences},
  edition = {3rd ed},
  publisher = {{Clarendon Press ; Oxford University Press}},
  address = {{Oxford [Oxfordshire] : New York}},
  isbn = {978-0-19-850368-2},
  langid = {english},
  lccn = {QA273 .J4 1998},
  keywords = {Probabilities},
  annotation = {10269},
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}

@article{jenkinson_2000,
  title = {Estimation of {{Smoothness}} from the {{Residual Field}}},
  author = {Jenkinson, Mark},
  year = {2000},
  pages = {6},
  abstract = {This report gives a detailed derivation of the smoothness estimation techniques used in Kiebel99 [2] and Forman95 [1].},
  langid = {english},
  annotation = {00009},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TQ4XUP8P\\Jenkinson - FMRIB Technical Report TR00MJ3.pdf}
}

@article{jenkinson_2012,
  title = {{{FSL}}},
  author = {Jenkinson, Mark and Beckmann, Christian F. and Behrens, Timothy E.J. and Woolrich, Mark W. and Smith, Stephen M.},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {2},
  pages = {782--790},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2011.09.015},
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}

@article{jensen_2005,
  title = {On the Human Sensorimotor-Cortex Beta Rhythm: {{Sources}} and Modeling},
  shorttitle = {On the Human Sensorimotor-Cortex Beta Rhythm},
  author = {Jensen, O and Goel, P and Kopell, N and Pohja, M and Hari, R and Ermentrout, B},
  year = {2005},
  month = jun,
  journal = {NeuroImage},
  volume = {26},
  number = {2},
  pages = {347--355},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2005.02.008},
  langid = {english},
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}

@article{jeong_2001,
  title = {Mutual Information Analysis of the {{EEG}} in Patients with {{Alzheimer}}'s Disease},
  author = {Jeong, Jaeseung and Gore, John C and Peterson, Bradley S},
  year = {2001},
  month = may,
  journal = {Clinical Neurophysiology},
  volume = {112},
  number = {5},
  pages = {827--835},
  issn = {13882457},
  doi = {10.1016/S1388-2457(01)00513-2},
  abstract = {Objective: Mutual information provides a measure of both the linear and nonlinear statistical dependencies between two time series. Cross-mutual information (CMI) is used to quantify the information transmitted from one time series to another, while auto mutual information (AMI) in a time series estimates how much on average the value of the time series can be predicted from values of the time series at preceding points. The aim of this study is to assess information transmission between different cortical areas in Alzheimer's disease (AD) patients by estimating the average CMI between EEG electrodes. Methods: We recorded the EEG from 16 scale electrodes in 15 AD patients and 15 age-matched normal controls, and estimated the local, distant, and interhemispheric CMIs of the EEG in both groups. The rate of decrease (with increasing delay) of the AMI of the EEG was also measured to evaluate the complexity of the EEG in AD patients. Results: The local CMI in AD subjects was lower than that in normal controls, especially over frontal and antero-temporal regions. A prominent decrease in information transmission between distant electrodes in the right hemisphere and between corresponding interhemispheric electrodes was detected in the AD patients. In addition, the AMIs throughout the cerebrums of the AD patients decreased signi\textregistered cantly more slowly with delay than did the AMIs of normal controls. Conclusions: These results are consistent with previous \textregistered ndings that suggest the association of EEG abnormalities in AD patients with functional impairment of information transmission in long cortico-cortical connections. q 2001 Elsevier Science Ireland Ltd. All rights reserved.},
  langid = {english},
  annotation = {00279},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\A3K9WXBF\\Jeong et al. - 2001 - Mutual information analysis of the EEG in patients.pdf}
}

@article{jeppersen-de_,
  title = {Prof. {{Dr}}. {{Madeleine}} de {{Cock Buning}}},
  author = {{Jeppersen-de}, Dr Christina H},
  pages = {31},
  annotation = {00000},
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}

@book{jezzard_2001,
  title = {Functional {{MRI}}: An Introduction to Methods},
  shorttitle = {Functional {{MRI}}},
  editor = {Jezzard, Peter and Matthews, Paul M. and Smith, Stephen M.},
  year = {2001},
  publisher = {{Oxford University Press}},
  address = {{Oxford ; New York}},
  isbn = {978-0-19-263071-1},
  lccn = {RC386.6.M34 F863 2001},
  keywords = {Brain,Magnetic resonance imaging},
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}

@article{jirsa_1996,
  title = {Field {{Theory}} of {{Electromagnetic Brain Activity}}},
  author = {Jirsa, V. K. and Haken, H.},
  year = {1996},
  month = jul,
  journal = {Physical Review Letters},
  volume = {77},
  number = {5},
  pages = {960--963},
  issn = {0031-9007, 1079-7114},
  doi = {10.1103/PhysRevLett.77.960},
  langid = {english},
  annotation = {00434},
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}

@article{jirsa_1997,
  title = {A Derivation of a Macroscopic Field Theory of the Brain from the Quasi-Microscopic Neural Dynamics},
  author = {Jirsa, V.K. and Haken, H.},
  year = {1997},
  month = jan,
  journal = {Physica D: Nonlinear Phenomena},
  volume = {99},
  number = {4},
  pages = {503--526},
  issn = {01672789},
  doi = {10.1016/S0167-2789(96)00166-2},
  abstract = {The purpose of this paper is twofold: First, we present a semi-quantitative nonlinear field theory of the brain under realistic anatomical connectivity conditions describing the interaction between functional units within the brain. This macroscopic field theory is derived from the quasi-microscopic conversion properties of neural populations occurring at synapses and somas. The quasi-microscopic models by Wilson-Cowan (1972,1973) and Nunez (1974) can be derived from these. Functional units are treated as inhomogeneities within a nonlinear one-dimensional neural tissue. Second, for the case of the Kelso experiment the field equation is treated analytically and numerically and can be reduced to a set of ordinary differential equations which corresponds to a model by Jirsa et al. (1994,1995). This phenomenological model reproduces the spatio-temporal phenomena experimentally observed. Here the most prominent property of the neural tissue is the parametric excitation. The macroscopic field parameters can be expressed by quasi-microscopic neural parameters.},
  langid = {english},
  annotation = {00229},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4MJQ9TJ7\\Jirsa and Haken - 1997 - A derivation of a macroscopic field theory of the .pdf}
}

@article{jirsa_2009,
  title = {Neural Field Dynamics with Local and Global Connectivity and Time Delay},
  author = {Jirsa, V. K},
  year = {2009},
  month = mar,
  journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  volume = {367},
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  doi = {10.1098/rsta.2008.0260},
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}

@article{joffily_2013,
  title = {Emotional {{Valence}} and the {{Free-Energy Principle}}},
  author = {Joffily, Mateus and Coricelli, Giorgio},
  editor = {Behrens, Tim},
  year = {2013},
  month = jun,
  journal = {PLoS Computational Biology},
  volume = {9},
  number = {6},
  pages = {e1003094},
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  doi = {10.1371/journal.pcbi.1003094},
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}

@article{johnson_,
  title = {A {{Dynamic Neural Field Theory}} of {{Multi-Item Visual Working Memory}} and {{Change Detection}}},
  author = {Johnson, Jeffrey S and Spencer, John P and Sch{\"o}ner, Gregor},
  pages = {6},
  abstract = {Many visually-guided behaviors rely critically on the ability to maintain visual information in working memory. However, to date, there are few formal models of visual working memory (VWM) that directly interface with the growing empirical literature on this basic cognitive system. In particular, no current theories address both the maintenance of multiple items in VWM and the process of change detection within a neurally-plausible framework. In the present study, we describe such an approach, along with initial data from a change detection task that confirm a novel prediction of our model.},
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}

@book{johnson_1994,
  title = {Continuous Univariate Distributions, {{Volume}} 1},
  author = {Johnson, John and Kotz and Balakrishnan, N.},
  year = {1994},
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@book{johnson_1994a,
  title = {Continuous Univariate Distributions, {{Volume}} 2},
  author = {Johnson, Norman Lloyd and Kotz, Samuel and Balakrishnan, N.},
  year = {1994},
  series = {Wiley Series in Probability and Mathematical Statistics},
  edition = {2nd ed},
  publisher = {{Wiley}},
  address = {{New York}},
  isbn = {978-0-471-58495-7 978-0-471-58494-0},
  langid = {english},
  lccn = {QA273.6 .J6 1994},
  keywords = {Distribution (Probability theory)},
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}

@book{johnson_2007,
  title = {Applied Multivariate Statistical Analysis},
  author = {Johnson, Richard A. and Wichern, Dean W.},
  year = {2007},
  edition = {6th ed},
  publisher = {{Pearson Prentice Hall}},
  address = {{Upper Saddle River, N.J}},
  isbn = {978-0-13-187715-3},
  langid = {english},
  lccn = {QA278 .J63 2007},
  keywords = {Multivariate analysis},
  annotation = {25277  OCLC: ocm70867129},
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}

@article{johnson_2008,
  title = {Moving to Higher Ground: {{The}} Dynamic Field Theory and the Dynamics of Visual Cognition},
  shorttitle = {Moving to Higher Ground},
  author = {Johnson, Jeffrey S. and Spencer, John P. and Sch{\"o}ner, Gregor},
  year = {2008},
  month = aug,
  journal = {New Ideas in Psychology},
  volume = {26},
  number = {2},
  pages = {227--251},
  issn = {0732118X},
  doi = {10.1016/j.newideapsych.2007.07.007},
  langid = {english},
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}

@article{johnson_a,
  title = {Applications of the {{Non-Central}} t-{{Distribution}}},
  author = {Johnson, N L and Welch, B L},
  pages = {29},
  langid = {english},
  annotation = {00363},
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}

@article{jolij_2017,
  title = {Privacy and Anonymity in Public Sharing of High-Dimensional Datasets: Legal and Ethical Restrictions},
  author = {Jolij, Jacob},
  year = {2017},
  journal = {PsyArxiv},
  pages = {22},
  abstract = {Over the past years, psychology has seen a remarkable move to increasing research transparency, following several high-profile cases of scientific misconduct and the realization that reproducibility of psychological research findings may be disappointingly low. Taking from exact science disciplines such as experimental physics, there is increasing support for public sharing of empirical data, with some researchers even arguing that public data sharing should be a requirement for publication. Indeed, the principle of data transparency is a core value in science. However, in psychology and cognitive neuroscience, researchers work with human subject data. This makes that data sharing is subject to legal and ethical restrictions. Here we discuss the legal and ethical implications of public data sharing, as advocated by several authors. We conclude that large scale public sharing leads to inevitable legal and ethical problems with participant privacy, especially in the light of continuous technological developments. What might be `anonymity' today could be become personal data tomorrow.},
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}

@article{jones_2002,
  title = {Changes in {{Blood Flow}}, {{Oxygenation}}, and {{Volume Following Extended Stimulation}} of {{Rodent Barrel Cortex}}},
  author = {Jones, Myles and Berwick, Jason and Mayhew, John},
  year = {2002},
  month = mar,
  journal = {NeuroImage},
  volume = {15},
  number = {3},
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  issn = {10538119},
  doi = {10.1006/nimg.2001.1000},
  langid = {english},
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}

@article{jones_2014,
  title = {Tactile Sensory System: Encoding from the Periphery to the Cortex: {{Tactile}} Sensory System},
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  author = {Jones, Lynette A. and Smith, Allan M.},
  year = {2014},
  month = may,
  journal = {Wiley Interdisciplinary Reviews: Systems Biology and Medicine},
  volume = {6},
  number = {3},
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  issn = {19395094},
  doi = {10.1002/wsbm.1267},
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}

@article{jongsma_2006,
  title = {Tracking Pattern Learning with Single-Trial Event-Related Potentials},
  author = {Jongsma, Marijtje L.A. and Eichele, Tom and Van Rijn, Clementina M. and Coenen, Anton M.L. and Hugdahl, Kenneth and Nordby, Helge and Quiroga, Rodrigo Quian},
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  number = {9},
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  doi = {10.1016/j.clinph.2006.05.012},
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}

@incollection{jordan_1998,
  title = {An {{Introduction}} to {{Variational Methods}} for {{Graphical Models}}},
  booktitle = {Learning in {{Graphical Models}}},
  author = {Jordan, Michael I. and Ghahramani, Zoubin and Jaakkola, Tommi S. and Saul, Lawrence K.},
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  year = {1998},
  pages = {105--161},
  publisher = {{Springer Netherlands}},
  address = {{Dordrecht}},
  doi = {10.1007/978-94-011-5014-9_5},
  abstract = {This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.},
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}

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@article{jorge_2014,
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@article{josem.bernardo_,
  title = {Statistical {{Inference}} as a {{Decision Problem}}: {{The Choice}} of {{Sample Size}}},
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@article{joyce_2012,
  title = {Development of {{PowerMap}}: A {{Software Package}} for {{Statistical Power Calculation}} in {{Neuroimaging Studies}}},
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  author = {Joyce, Karen E. and Hayasaka, Satoru},
  year = {2012},
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  volume = {10},
  number = {4},
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  doi = {10.1007/s12021-012-9152-3},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Y32QFPWS\\Joyce_NeuroInf_12.pdf}
}

@article{kackar_1981,
  title = {Unbiasedness of Two-Stage Estimation and Prediction Procedures for Mixed Linear Models},
  author = {Kackar, Raghu N. and Harville, David A.},
  year = {1981},
  journal = {Communications in Statistics - Theory and Methods},
  volume = {10},
  number = {13},
  pages = {1249--1261},
  issn = {0361-0926},
  doi = {10.1080/03610928108828108},
  abstract = {The traditional method for estimating or predicting linear combinations of the fixed effects and realized values of the random effects in mixed linear models is first to estimate the variance components and then to proceed as if the estimated values of the variance components were the true values. This two-stage procedure gives unbiased estimators or predictors of the linear combinations provided the data vector is symmetrically distributed about its expected value and provided the variance component estimators are translation-invariant and are even functions of the data vector. The standard procedures for estimating the variance components yield even, translation-invariant estimators.},
  langid = {english},
  annotation = {00189},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\H3V3K95Q\\Kackar und Harville - 1981 - Unbiasedness of two-stage estimation and predictio.pdf}
}

@article{kaelbling_1998,
  title = {Planning and Acting in Partially Observable Stochastic Domains},
  author = {Kaelbling, Leslie Pack and Littman, Michael L. and Cassandra, Anthony R.},
  year = {1998},
  month = may,
  journal = {Artificial Intelligence},
  volume = {101},
  number = {1-2},
  pages = {99--134},
  issn = {00043702},
  doi = {10.1016/S0004-3702(98)00023-X},
  abstract = {In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (MDPs) and partially observable MDPs (POMDPs). We then outline a novel algorithm for solving POMDPs off line and show how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP. We conclude with a discussion of how our approach relates to previous work, the complexity of finding exact solutions to POMDPs, and of some possibilities for finding approximate solutions. \textcopyright{} 1998 Elsevier Science B.V. All rights reserved.},
  langid = {english},
  annotation = {03088},
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}

@article{kahnt_2013,
  title = {Salience {{Signals}} in the {{Right Temporoparietal Junction Facilitate Value-Based Decisions}}},
  author = {Kahnt, T. and Tobler, P. N.},
  year = {2013},
  month = jan,
  journal = {Journal of Neuroscience},
  volume = {33},
  number = {3},
  pages = {863--869},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3531-12.2013},
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}

@article{kaiser_2007,
  title = {Dynamics of {{Oscillatory Activity}} during {{Auditory Decision Making}}},
  author = {Kaiser, J. and Lennert, T. and Lutzenberger, W.},
  year = {2007},
  month = oct,
  journal = {Cerebral Cortex},
  volume = {17},
  number = {10},
  pages = {2258--2267},
  issn = {1047-3211, 1460-2199},
  doi = {10.1093/cercor/bhl134},
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}

@book{kallenberg_2021,
  title = {Foundations of {{Modern Probability}}},
  author = {Kallenberg, Olav},
  year = {2021},
  series = {Probability {{Theory}} and {{Stochastic Modelling}}},
  volume = {99},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-61871-1},
  isbn = {978-3-030-61870-4 978-3-030-61871-1},
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}

@article{kalman_1960,
  title = {A {{New Approach}} to {{Linear Filtering}} and {{Prediction Problems}}},
  author = {Kalman, R. E.},
  year = {1960},
  journal = {Journal of Basic Engineering},
  volume = {82},
  number = {1},
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  issn = {00219223},
  doi = {10.1115/1.3662552},
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  annotation = {27367},
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}

@article{kamitani_2005,
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  author = {Kamitani, Yukiyasu and Tong, Frank},
  year = {2005},
  month = may,
  journal = {Nature Neuroscience},
  volume = {8},
  number = {5},
  pages = {679--685},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn1444},
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}

@book{kandel_2013,
  title = {Principles of Neural Science},
  author = {Kandel, Eric R},
  year = {2013},
  publisher = {{McGraw-Hill Medical}},
  address = {{New York; Toronto}},
  abstract = {"The field's definitive work from a Nobel Prize-winning author 900 full-color illustrations Principles of Neural Science, it describes our current understanding of how the nerves, brain, and mind function. From molecules to anatomic structures and systems to cognitive function, this comprehensive reference covers all aspects of neuroscience. Widely regarded as the field's cornerstone reference, the fifth edition is highlighted by more than 900 full-color illustrations. The fifth edition has been completely updated to reflect the tremendous amount of new research and development in neuroscience in the last decade. Lead author Eric Kandel was awarded the Nobel Prize in Physiology or Medicine in 2000"--R\'esum\'e de l'\'editeur.},
  isbn = {978-0-07-139011-8 978-0-07-181001-2 978-1-283-65624-5},
  langid = {english},
  annotation = {00000  OCLC: 852737181},
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}

@article{kanwisher_1997,
  title = {The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception},
  shorttitle = {The Fusiform Face Area},
  author = {Kanwisher, Nancy and McDermott, Josh and Chun, Marvin M.},
  year = {1997},
  journal = {Journal of neuroscience},
  volume = {17},
  number = {11},
  pages = {4302--4311},
  annotation = {06529},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MZ3CHRL7\\Kanwisher_JNeurosci_97.pdf}
}

@article{kao_2009,
  title = {Multi-Objective Optimal Experimental Designs for Event-Related {{fMRI}} Studies},
  author = {Kao, Ming-Hung and Mandal, Abhyuday and Lazar, Nicole and Stufken, John},
  year = {2009},
  month = feb,
  journal = {NeuroImage},
  volume = {44},
  number = {3},
  pages = {849--856},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.09.025},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CP76GCTA\\Kao et al_2009_NeuroImage_Multi-objective optimal experimental designs for event-related fMRI studies.pdf}
}

@article{kaplan_2017,
  title = {The {{Role}} of {{Mental Maps}} in {{Decision-Making}}},
  author = {Kaplan, Raphael and Schuck, Nicolas W. and Doeller, Christian F.},
  year = {2017},
  journal = {Trends in Neurosciences},
  volume = {40},
  number = {5},
  pages = {256--259},
  issn = {1878108X},
  doi = {10.1016/j.tins.2017.03.002},
  abstract = {A growing body of work is investigating the use of mental maps during decision-making. Here we discuss how decision-making organizes experiences according to an internal model of the current task, thereby structuring memory. Likewise, we consider how the structure of mental maps contributes to decision-making.},
  pmid = {28365032},
  keywords = {cognitive map,decision-making,entorhinal cortex,hippocampus,medial prefrontal cortex,state space},
  annotation = {00000}
}

@article{kaplan_2018,
  title = {Planning and Navigation as Active Inference},
  author = {Kaplan, Raphael and Friston, Karl J.},
  year = {2018},
  journal = {Biological Cybernetics},
  pages = {1--21},
  issn = {14320770},
  doi = {10.1007/s00422-018-0753-2},
  abstract = {This paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation-exploration dilemma is dissolved by acting to minimise uncertainty (i.e., expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour, driven by novelty and the imperative to reduce uncertainty about the world, contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between place cells that fire when a subgoal is reached, and path cells, that fire until a subgoal is reached.},
  pmid = {29572721},
  keywords = {Active inference,Bayesian,Curiosity,Epistemic value,Exploitation,Exploration,Free energy,Novelty,Salience}
}

@article{kappen_2005,
  title = {Path Integrals and Symmetry Breaking for Optimal Control Theory},
  author = {Kappen, H J},
  year = {2005},
  month = nov,
  journal = {Journal of Statistical Mechanics: Theory and Experiment},
  volume = {2005},
  number = {11},
  pages = {P11011-P11011},
  issn = {1742-5468},
  doi = {10.1088/1742-5468/2005/11/P11011},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\W5LNZC2N\\Kappen 2005.pdf}
}

@book{kariya_2004,
  title = {Generalized Least Squares},
  author = {Kariya, Takeaki and Kurata, Hiroshi},
  year = {2004},
  series = {Wiley Series in Probability and Statistics},
  publisher = {{Wiley}},
  address = {{Chichester, West Sussex, England ; Hoboken, NJ}},
  isbn = {978-0-470-86697-9},
  lccn = {QA275 .K32 2004},
  keywords = {Least squares},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JKB7DJ44\\Kariya_Kurata_2004_Generalized least squares.pdf}
}

@book{karlin_1975,
  title = {A First Course in Stochastic Processes},
  author = {Karlin, Samuel and Taylor, Howard M.},
  year = {1975},
  edition = {2d ed},
  publisher = {{Academic Press}},
  address = {{New York}},
  isbn = {978-0-12-398552-1},
  lccn = {QA274 .K37 1975},
  keywords = {Stochastic processes},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3247NBQ8\\Karlin_Taylor_1975_A first course in stochastic processes.pdf}
}

@article{karpac_2005,
  title = {Development, {{Maintenance}}, and {{Function}} of the {{Adrenal Gland}} in {{Early Postnatal Proopiomelanocortin-Null Mutant Mice}}},
  author = {Karpac, Jason and Ostwald, Dirk and Bui, Stephanie and Hunnewell, Peggy and Shankar, Malini and Hochgeschwender, Ute},
  year = {2005},
  month = jun,
  journal = {Endocrinology},
  volume = {146},
  number = {6},
  pages = {2555--2562},
  issn = {0013-7227, 1945-7170},
  doi = {10.1210/en.2004-1290},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5HD75EPE\\Karpac et al_2005_Endocrinology_Development, Maintenance, and Function of the Adrenal Gland in Early Postnatal.pdf;C\:\\Users\\dirko\\Zotero\\storage\\ZUBUPUXP\\Karpac et al. - 2005 - Development, Maintenance, and Function of the Adre.pdf}
}

@article{karpac_2006,
  title = {Proopiomelanocortin Heterozygous and Homozygous Null Mutant Mice Develop Pituitary Adenomas},
  author = {Karpac, J. and Ostwald, D. and Li, G.-Y. and Bui, S. and Hunnewell, P. and Brennan, M. B. and Hochgeschwender, U.},
  year = {2006},
  month = may,
  journal = {Cellular and Molecular Biology (Noisy-Le-Grand, France)},
  volume = {52},
  number = {2},
  pages = {47--52},
  issn = {1165-158X},
  abstract = {Mice lacking all pro-opiomelanocortin (POMC)-derived peptides have been created by gene targeting of the POMC locus in embryonic stem cells. Phenotypes of the POMC null homozygous mutants include obesity, pigmentation defects, and adrenal insufficiency. Here, we report that both POMC null homozygous and heterozygous mutants also develop pituitary gland tumors, which result in their premature death. The tumors occur with 100\% penetrance in both POMC heterozygous and homozygous genotypes. Histological examinations reveal that tumors start from hyperplastic focal points of melanotrophic cells within the intermediate lobe. Based on the morphological and immunohistological features, we have classified the tumors as non-invasive, non-secreting, intermediate lobe adenomas. These findings uncover potential novel roles of melanocortins in the regulation of cell proliferation.},
  langid = {english},
  pmid = {16914086},
  keywords = {Adenoma,Adrenocorticotropic Hormone,alpha-MSH,Animals,Disease Progression,Heterozygote,Homozygote,Immunohistochemistry,In Situ Hybridization,Mice,Mice; Knockout,Pituitary Neoplasms,Pro-Opiomelanocortin,Survival Analysis,Survival Rate},
  annotation = {00007}
}

@article{kaspi_1997,
  title = {Markov {{Chains}} and {{Stochastic Stability}}},
  author = {Kaspi, Haya and Meyn, S. P. and Tweedie, R. L.},
  year = {1997},
  month = jun,
  journal = {Journal of the American Statistical Association},
  volume = {92},
  number = {438},
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  issn = {01621459},
  doi = {10.2307/2965732},
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}

@article{kass_1989,
  title = {Approximate {{Bayesian Inference}} in {{Conditionally Independent Hierarchical Models}} ({{Parametric Empirical Bayes Models}})},
  author = {Kass, Robert E. and Steffey, Duane},
  year = {1989},
  month = sep,
  journal = {Journal of the American Statistical Association},
  volume = {84},
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}

@article{kass_1995,
  title = {Bayes {{Factors}}},
  author = {Kass, Robert E. and Raftery, Adrian E.},
  year = {1995},
  month = jun,
  journal = {Journal of the American Statistical Association},
  volume = {90},
  number = {430},
  pages = {773--795},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.1995.10476572},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\RCFL9NL8\\Kass und Raftery - 1995 - Bayes Factors.pdf}
}

@article{kass_1996,
  title = {The {{Selection}} of {{Prior Distributions}} by {{Formal Rules}}},
  author = {Kass, Robert E. and Wasserman, Larry},
  year = {1996},
  month = sep,
  journal = {Journal of the American Statistical Association},
  volume = {91},
  number = {435},
  pages = {1343},
  issn = {01621459},
  doi = {10.2307/2291752},
  annotation = {00000},
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}

@article{katz_2016,
  title = {Dissociated Functional Significance of Decision-Related Activity in the Primate Dorsal Stream},
  author = {Katz, Leor N. and Yates, Jacob L. and Pillow, Jonathan W. and Huk, Alexander C.},
  year = {2016},
  month = jul,
  journal = {Nature},
  volume = {535},
  number = {7611},
  pages = {285--288},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature18617},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HLAU25E2\\Katz et al_2016_Nature_Dissociated functional significance of decision-related activity in the primate.pdf}
}

@article{kaye_2012,
  title = {The {{Tension Between Data Sharing}} and the {{Protection}} of {{Privacy}} in {{Genomics Research}}},
  author = {Kaye, Jane},
  year = {2012},
  month = sep,
  journal = {Annual Review of Genomics and Human Genetics},
  volume = {13},
  number = {1},
  pages = {415--431},
  issn = {1527-8204, 1545-293X},
  doi = {10.1146/annurev-genom-082410-101454},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AA6ZYQX7\\Kaye - 2012 - The Tension Between Data Sharing and the Protectio.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UH6EB63Y\\Kaye 2012 The Tension Between Data Sharing and the Protection of Privacy in Genomics Research.pdf;C\:\\Users\\dirko\\Zotero\\storage\\V92LNIYU\\Kaye - 2012 - The Tension Between Data Sharing and the Protectio.pdf}
}

@article{kaye_2015,
  title = {Dynamic Consent: A Patient Interface for Twenty-First Century Research Networks},
  shorttitle = {Dynamic Consent},
  author = {Kaye, Jane and Whitley, Edgar A and Lund, David and Morrison, Michael and Teare, Harriet and Melham, Karen},
  year = {2015},
  month = feb,
  journal = {European Journal of Human Genetics},
  volume = {23},
  number = {2},
  pages = {141--146},
  issn = {1018-4813, 1476-5438},
  doi = {10.1038/ejhg.2014.71},
  langid = {english},
  annotation = {00190},
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}

@article{kayser_2010,
  title = {In Search of the {{Rosetta Stone}} for Scalp {{EEG}}: {{Converging}} on Reference-Free Techniques},
  shorttitle = {In Search of the {{Rosetta Stone}} for Scalp {{EEG}}},
  author = {Kayser, J{\"u}rgen and Tenke, Craig E.},
  year = {2010},
  month = dec,
  journal = {Clinical Neurophysiology},
  volume = {121},
  number = {12},
  pages = {1973--1975},
  issn = {13882457},
  doi = {10.1016/j.clinph.2010.04.030},
  langid = {english},
  annotation = {00049},
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}

@article{kayser_2010a,
  title = {Neural {{Representations}} of {{Relevant}} and {{Irrelevant Features}} in {{Perceptual Decision Making}}},
  author = {Kayser, A. S. and Erickson, D. T. and Buchsbaum, B. R. and D'Esposito, M.},
  year = {2010},
  month = nov,
  journal = {Journal of Neuroscience},
  volume = {30},
  number = {47},
  pages = {15778--15789},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3163-10.2010},
  langid = {english},
  annotation = {00000},
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}

@article{kayser_2010b,
  title = {The {{Functional Anatomy}} of a {{Perceptual Decision}} in the {{Human Brain}}},
  author = {Kayser, Andrew S. and Buchsbaum, Bradley R. and Erickson, Drew T. and D'Esposito, Mark},
  year = {2010},
  month = mar,
  journal = {Journal of Neurophysiology},
  volume = {103},
  number = {3},
  pages = {1179--1194},
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  doi = {10.1152/jn.00364.2009},
  langid = {english},
  annotation = {00000},
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}

@article{keil_2014,
  title = {Committee Report: {{Publication}} Guidelines and Recommendations for Studies Using Electroencephalography and Magnetoencephalography: {{Guidelines}} for {{EEG}} and {{MEG}}},
  shorttitle = {Committee Report},
  author = {Keil, Andreas and Debener, Stefan and Gratton, Gabriele and Jungh{\"o}fer, Markus and Kappenman, Emily S. and Luck, Steven J. and Luu, Phan and Miller, Gregory A. and Yee, Cindy M.},
  year = {2014},
  month = jan,
  journal = {Psychophysiology},
  volume = {51},
  number = {1},
  pages = {1--21},
  issn = {00485772},
  doi = {10.1111/psyp.12147},
  langid = {english},
  annotation = {00000},
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}

@article{kekoni_,
  title = {{{PROCESSING OF STIMULUS REPETITION AND CHANGE IN THE SOMATOSENSORY SYSTEM}}: {{RECORDINGS OF ELECTRICAL AND MAGNETIC BRAIN RESPONSES}}},
  author = {KEKONI, JOUNI},
  pages = {35},
  annotation = {00004},
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}

@article{kell_2018,
  title = {A {{Task-Optimized Neural Network Replicates Human Auditory Behavior}}, {{Predicts Brain Responses}}, and {{Reveals}} a {{Cortical Processing Hierarchy}}},
  author = {Kell, Alexander J.E. and Yamins, Daniel L.K. and Shook, Erica N. and {Norman-Haignere}, Sam V. and McDermott, Josh H.},
  year = {2018},
  month = apr,
  journal = {Neuron},
  issn = {08966273},
  doi = {10.1016/j.neuron.2018.03.044},
  abstract = {A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy\textemdash primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EL944FCH\\Kell et al. - 2018 - A Task-Optimized Neural Network Replicates Human A.pdf}
}

@article{kellen_2019,
  title = {A {{Model Hierarchy}} for {{Psychological Science}}},
  author = {Kellen, David},
  year = {2019},
  month = dec,
  journal = {Computational Brain \& Behavior},
  volume = {2},
  number = {3-4},
  pages = {160--165},
  issn = {2522-0861, 2522-087X},
  doi = {10.1007/s42113-019-00037-y},
  abstract = {Lee et al. (2019) provided a comprehensive list of recommendations for modelers that aims at improving the robustness of their results. Drawing from the literature on philosophy of science, the present commentary argues for a broader view of modeling that considers the different roles that they play in our scientific practices. Following Suppes (1966), I propose a model hierarchy and discuss the distinct issues that arise at each of its levels. The benefit of a hierarchy of this kind is that it can aid researchers in better understanding the different challenges that they face.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6SLQEFIC\\Kellen - 2019 - A Model Hierarchy for Psychological Science.pdf}
}

@article{kempka_2016,
  title = {{{ViZDoom}}: {{A Doom-based AI Research Platform}} for {{Visual Reinforcement Learning}}},
  shorttitle = {{{ViZDoom}}},
  author = {Kempka, Micha{\l} and Wydmuch, Marek and Runc, Grzegorz and Toczek, Jakub and Ja{\'s}kowski, Wojciech},
  year = {2016},
  month = sep,
  journal = {arXiv:1605.02097 [cs]},
  eprint = {1605.02097},
  eprinttype = {arxiv},
  primaryclass = {cs},
  abstract = {The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective. Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the firstperson perspective in a semi-realistic 3D world. The software, called ViZDoom, is based on the classical first-person shooter video game, Doom. It allows developing bots that play the game using the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a convenient mechanism of user scenarios. In the experimental part, we test the environment by trying to learn bots for two scenarios: a basic move-and-shoot task and a more complex maze-navigation problem. Using convolutional deep neural networks with Q-learning and experience replay, for both scenarios, we were able to train competent bots, which exhibit human-like behaviors. The results confirm the utility of ViZDoom as an AI research platform and imply that visual reinforcement learning in 3D realistic first-person perspective environments is feasible.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  annotation = {00415},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4Q8U2BB3\\Kempka et al. - 2016 - ViZDoom A Doom-based AI Research Platform for Vis.pdf}
}

@article{kenemans_2000,
  title = {On the Processing of Spatial Frequencies as Revealed by Evoked-Potential Source Modelingq},
  author = {Kenemans, J L and Baas, J M P and Mangun, G R and Lijf, M},
  year = {2000},
  journal = {Clinical Neurophysiology},
  pages = {11},
  abstract = {Objectives and methods: Visually evoked potentials (VEPs) are known to be sensitive to spatial frequency, especially in the time range between 50 and 100 ms post-stimulus. In two experiments we localized the cortical activity elicited by stimuli of varying spatial frequency in scalp-recorded brain potentials, using multi-electrode recordings and dipole-source analysis. Results: Low spatial frequencies (,1 c/d) activated relatively lateral occipital areas, the orientation of the neural ensembles involved being predominantly perpendicular to the scalp surface. In contrast, high spatial frequencies (.4 c/d) induced activation of more medial occipital areas with the predominant orientation of the sources being much more parallel to the scalp surface. Furthermore, at about 100 ms latency the lateral-occipital response to low spatial frequencies was stronger in the right hemisphere; no such asymmetry was found for the responses to the high spatial frequencies. These \textregistered ndings were consistent across varying recording conditions, individual subjects, subject populations, stimulus characteristics (grating orientation, grating vs. checkerboard), and task conditions (active vs. passive). Conclusion: The results indicate that there are differences in sensitivity to speci\textregistered c spatial frequencies between primary and secondary visual areas, as well as between the right and the left hemispheres. q 2000 Elsevier Science Ireland Ltd. All rights reserved.},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AYIALB5F\\Kenemans et al. - 2000 - On the processing of spatial frequencies as reveal.pdf;C\:\\Users\\dirko\\Zotero\\storage\\U8AA4965\\Kenemans et al. - 2000 - On the processing of spatial frequencies as reveal.pdf}
}

@article{kennedy-shaffer_2019,
  title = {Before {$<$} 0.05 to {{Beyond}}{$<$} 0.05: {{Using History}} to {{Contextualize}} p -{{Values}} and {{Significance Testing}}},
  shorttitle = {Before {\emph{p}} {$<$} 0.05 to {{Beyond}} {\emph{p}} {$<$} 0.05},
  author = {{Kennedy-Shaffer}, Lee},
  year = {2019},
  month = mar,
  journal = {The American Statistician},
  volume = {73},
  number = {sup1},
  pages = {82--90},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2018.1537891},
  abstract = {As statisticians and scientists consider a world beyond p {$<$} 0.05, it is important to not lose sight of how we got to this point. Although significance testing and p-values are often presented as prescriptive procedures, they came about through a process of refinement and extension to other disciplines. Ronald A. Fisher and his contemporaries formalized these methods in the early twentieth century and Fisher's 1925 Statistical Methods for Research Workers brought the techniques to experimentalists in a variety of disciplines. Understanding how these methods arose, spread, and were argued over since then illuminates how p {$<$} 0.05 came to be a standard for scientific inference, the advantage it offered at the time, and how it was interpreted. This historical perspective can inform the work of statisticians today by encouraging thoughtful consideration of how their work, including proposed alternatives to the p-value, will be perceived and used by scientists. And it can engage students more fully and encourage critical thinking rather than rote applications of formulae. Incorporating history enables students, practitioners, and statisticians to treat the discipline as an ongoing endeavor, crafted by fallible humans, and provides a deeper understanding of the subject and its consequences for science and society.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Q3X585KL\\Kennedy-Shaffer - 2019 - Before ipi  0.05 to Beyond ipi  0.05 .pdf}
}

@article{kepler_1992,
  title = {Reduction of Conductance-Based Neuron Models},
  author = {Kepler, Thomas B. and Abbott, L. F. and Marder, Eve},
  year = {1992},
  month = mar,
  journal = {Biological Cybernetics},
  volume = {66},
  number = {5},
  pages = {381--387},
  issn = {0340-1200, 1432-0770},
  doi = {10.1007/BF00197717},
  abstract = {We present a scheme for systematically reducing the number of differential equations required for biophysically realistic neuron models. The techniques are general, are designed to be applicable to a large set of such models and retain in the reduced system as high a degree of fidelity to the original system as possible. As examples, we provide reductions of the Hodgkin-Huxley system and the A-current model of Connor et al. (1977).},
  langid = {english},
  annotation = {00208},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9CDRTAWJ\\Kepler et al. - 1992 - Reduction of conductance-based neuron models.pdf}
}

@book{kerlinger_1986,
  title = {Foundations of Behavioral Research},
  author = {Kerlinger, Fred N.},
  year = {1986},
  edition = {3rd ed},
  publisher = {{Holt, Rinehart and Winston}},
  address = {{New York}},
  isbn = {978-0-03-041761-0},
  lccn = {BF76.5 .K4 1986},
  keywords = {Psychology,Research,Sociology,Statistical methods},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FIVJ5GY5\\2015.111808.Foundations-Of-Behavioral-Research-Edition-Second.pdf}
}

@article{kersten_2004,
  title = {Object {{Perception}} as {{Bayesian Inference}}},
  author = {Kersten, Daniel and Mamassian, Pascal and Yuille, Alan},
  year = {2004},
  month = feb,
  journal = {Annual Review of Psychology},
  volume = {55},
  number = {1},
  pages = {271--304},
  issn = {0066-4308, 1545-2085},
  doi = {10.1146/annurev.psych.55.090902.142005},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\T64HLMRI\\Kersten et al_2004_Annual Review of Psychology_Object Perception as Bayesian Inference.pdf}
}

@article{keselman_2001,
  title = {The Analysis of Repeated Measures Designs: {{A}} Review},
  shorttitle = {The Analysis of Repeated Measures Designs},
  author = {Keselman, H. J. and Algina, James and Kowalchuk, Rhonda K.},
  year = {2001},
  month = may,
  journal = {British Journal of Mathematical and Statistical Psychology},
  volume = {54},
  number = {1},
  pages = {1--20},
  issn = {2044-8317},
  doi = {10.1348/000711001159357},
  abstract = {Repeated measures ANOVA can refer to many different types of analysis. Specifically, this vague term can refer to conventional tests of significance, one of three univariate solutions with adjusted degrees of freedom, two different types of multivariate statistic, or approaches that combine univariate and multivariate tests. Accordingly, it is argued that, by only reporting probability values and referring to statistical analyses as repeated measures ANOVA, authors convey neither the type of analysis that was used nor the validity of the reported probability value, since each of these approaches has its own strengths and weaknesses. The various approaches are presented with a discussion of their strengths and weaknesses, and recommendations are made regarding the `best' choice of analysis. Additional topics discussed include analyses for missing data and tests of linear contrasts.},
  langid = {english},
  annotation = {00247},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QWANHZG2\\Keselman et al_2001_British Journal of Mathematical and Statistical Psychology_The analysis of repeated measures designs.pdf;C\:\\Users\\dirko\\Zotero\\storage\\FUD9VWXL\\abstract.html}
}

@article{kessler_2017,
  title = {Reevaluating ``Cluster Failure'' in {{fMRI}} Using Nonparametric Control of the False Discovery Rate},
  author = {Kessler, Daniel and Angstadt, Mike and Sripada, Chandra S.},
  year = {2017},
  month = apr,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {114},
  number = {17},
  pages = {E3372-E3373},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1614502114},
  abstract = {In a substantial contribution to the fMRI field, Eklund et al. (1) use nonparametric methods to demonstrate that random field theory (RFT)-based familywise error (FWE) correction for cluster inference does not control errors appropriately, and this discrepancy is more pronounced for lenient cluster-defining thresholds (CDT). Moreover, they point to violations of RFT assumptions as the culprit for this discrepancy. Given these results, how should we interpret existing fMRI literature that used RFT-based, FWE-corrected P values ({$<$}mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"{$><$}mml:mrow{$><$}mml:msub{$><$}mml:mi{$>$}p{$<$}/mml:mi{$><$}mml:mrow{$><$}mml:mtext{$>$}RFT{$<$}/mml:mtext{$><$}mml:mo{$>$}-{$<$}/mml:mo{$><$}mml:mtext{$>$}FWE{$<$}/mml:mtext{$><$}/mml:mrow{$><$}/mml:msub{$><$}/mml:mrow{$><$}/mml:math{$>$})? To suggest caution is reasonable but incomplete; we require concrete, quantitative guidelines to enable appropriate calibration of skepticism. Here, we undertake an initial attempt at such guidance. We heed Eklund et al.'s (1) warning and prefer nonparametric null distributions to RFT. However, we focus on the false discovery rate (FDR) (2 \ldots{}  [{$\carriagereturn$}][1]2To whom correspondence should be addressed. Email: kesslerd\{at\}umich.edu.  [1]: \#xref-corresp-1-1},
  copyright = {\textcopyright{}  . http://www.pnas.org/site/misc/userlicense.xhtml},
  langid = {english},
  pmid = {28420796},
  annotation = {00011},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3AANHNZA\\Kessler et al_2017_Proceedings of the National Academy of Sciences_Reevaluating “cluster failure” in fMRI using nonparametric control of the false.pdf;C\:\\Users\\dirko\\Zotero\\storage\\FZHGL9MK\\E3372.html}
}

@article{khader_2011,
  title = {Memory-Based {{Decision-making}} with {{Heuristics}}: {{Evidence}} for a {{Controlled Activation}} of {{Memory Representations}}},
  shorttitle = {Memory-Based {{Decision-making}} with {{Heuristics}}},
  author = {Khader, Patrick H. and Pachur, Thorsten and Meier, Stefanie and Bien, Siegfried and Jost, Kerstin and R{\"o}sler, Frank},
  year = {2011},
  month = nov,
  journal = {Journal of Cognitive Neuroscience},
  volume = {23},
  number = {11},
  pages = {3540--3554},
  issn = {0898-929X, 1530-8898},
  doi = {10.1162/jocn_a_00059},
  langid = {english},
  annotation = {00047},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9K7NY3JY\\Khader et al. - 2011 - Memory-based Decision-making with Heuristics Evid.pdf}
}

@article{khader_2016,
  title = {Neural {{Signatures}} of {{Controlled}} and {{Automatic Retrieval Processes}} in {{Memory-based Decision-making}}},
  author = {Khader, Patrick H. and Pachur, Thorsten and Weber, Lilian A. E. and Jost, Kerstin},
  year = {2016},
  month = jan,
  journal = {Journal of Cognitive Neuroscience},
  volume = {28},
  number = {1},
  pages = {69--83},
  issn = {0898-929X, 1530-8898},
  doi = {10.1162/jocn_a_00882},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LAXN4BR2\\Khader et al_2016_Journal of Cognitive Neuroscience_Neural Signatures of Controlled and Automatic Retrieval Processes in.pdf}
}

@article{khodadadi_2014,
  title = {Learning to Maximize Reward Rate: A Model Based on Semi-{{Markov}} Decision Processes},
  shorttitle = {Learning to Maximize Reward Rate},
  author = {Khodadadi, Arash and Fakhari, Pegah and Busemeyer, Jerome R.},
  year = {2014},
  month = may,
  journal = {Frontiers in Neuroscience},
  volume = {8},
  issn = {1662-453X},
  doi = {10.3389/fnins.2014.00101},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JWJ9MN5W\\Khodadadi_FIN_14.pdf}
}

@article{kiani_2008,
  title = {Bounded {{Integration}} in {{Parietal Cortex Underlies Decisions Even When Viewing Duration Is Dictated}} by the {{Environment}}},
  author = {Kiani, R. and Hanks, T. D. and Shadlen, M. N.},
  year = {2008},
  month = mar,
  journal = {Journal of Neuroscience},
  volume = {28},
  number = {12},
  pages = {3017--3029},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.4761-07.2008},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TXSMGHPU\\Kiani et al_2008_Journal of Neuroscience_Bounded Integration in Parietal Cortex Underlies Decisions Even When Viewing.pdf}
}

@article{kida_2004,
  title = {Passive Enhancement of the Somatosensory {{P100}} and {{N140}} in an Active Attention Task Using Deviant Alone Condition},
  author = {Kida, Tetsuo and Nishihira, Yoshiaki and Wasaka, Toshiaki and Nakata, Hiroki and Sakamoto, Masanori},
  year = {2004},
  month = apr,
  journal = {Clinical Neurophysiology},
  volume = {115},
  number = {4},
  pages = {871--879},
  issn = {13882457},
  doi = {10.1016/j.clinph.2003.11.037},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\E6PE8NJP\\Kida et al_2004_Clinical Neurophysiology_Passive enhancement of the somatosensory P100 and N140 in an active attention.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PPRG9U3Z\\Kida et al_2004_Clinical Neurophysiology_Passive enhancement of the somatosensory P100 and N140 in an active attention.pdf}
}

@article{kidger_2020,
  title = {Universal {{Approximation}} with {{Deep Narrow Networks}}},
  author = {Kidger, Patrick and Lyons, Terry},
  year = {2020},
  month = jun,
  journal = {arXiv:1905.08539 [cs, math, stat]},
  eprint = {1905.08539},
  eprinttype = {arxiv},
  primaryclass = {cs, math, stat},
  abstract = {The classical Universal Approximation Theorem holds for neural networks of arbitrary width and bounded depth. Here we consider the natural `dual' scenario for networks of bounded width and arbitrary depth. Precisely, let n be the number of inputs neurons, m be the number of output neurons, and let {$\rho$} be any nonaffine continuous function, with a continuous nonzero derivative at some point. Then we show that the class of neural networks of arbitrary depth, width n + m + 2, and activation function {$\rho$}, is dense in C(K; Rm) for K {$\subseteq$} Rn with K compact. This covers every activation function possible to use in practice, and also includes polynomial activation functions, which is unlike the classical version of the theorem, and provides a qualitative difference between deep narrow networks and shallow wide networks. We then consider several extensions of this result. In particular we consider nowhere differentiable activation functions, density in noncompact domains with respect to the Lp-norm, and how the width may be reduced to just n + m + 1 for `most' activation functions.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {41A46; 41A63; 68T07,Computer Science - Machine Learning,Mathematics - Classical Analysis and ODEs,Statistics - Machine Learning},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Z5WX4VPH\\Kidger und Lyons - 2020 - Universal Approximation with Deep Narrow Networks.pdf}
}

@article{kiebel_1999,
  title = {Robust Smoothness Estimation in Statistical Parametric Maps Using Standardized Residuals from the General Linear Model},
  author = {Kiebel, Stefan J. and Poline, Jean-Baptiste and Friston, Karl J. and Holmes, Andrew P. and Worsley, Keith J.},
  year = {1999},
  journal = {Neuroimage},
  volume = {10},
  number = {6},
  pages = {756--766},
  annotation = {00236},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\X8B3NCMM\\Kiebel et al_1999_Neuroimage_Robust smoothness estimation in statistical parametric maps using standardized.pdf}
}

@article{kiebel_2006,
  title = {Dynamic Causal Modelling of Evoked Responses in {{EEG}}/{{MEG}} with Lead Field Parameterization},
  author = {Kiebel, Stefan J. and David, Olivier and Friston, Karl J.},
  year = {2006},
  month = may,
  journal = {NeuroImage},
  volume = {30},
  number = {4},
  pages = {1273--1284},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2005.12.055},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Z52JTCRK\\Kiebel et al_2006_NeuroImage_Dynamic causal modelling of evoked responses in EEG-MEG with lead field.pdf}
}

@article{kiebel_2007,
  title = {Dynamic Causal Modelling of Evoked Responses: {{The}} Role of Intrinsic Connections},
  shorttitle = {Dynamic Causal Modelling of Evoked Responses},
  author = {Kiebel, Stefan J. and Garrido, Marta I. and Friston, Karl J.},
  year = {2007},
  month = jun,
  journal = {NeuroImage},
  volume = {36},
  number = {2},
  pages = {332--345},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.02.046},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XB6F9LLH\\Kiebel et al_2007_NeuroImage_Dynamic causal modelling of evoked responses.pdf}
}

@article{kiebel_2008,
  title = {A {{Hierarchy}} of {{Time-Scales}} and the {{Brain}}},
  author = {Kiebel, Stefan J. and Daunizeau, Jean and Friston, Karl J.},
  editor = {Sporns, Olaf},
  year = {2008},
  month = nov,
  journal = {PLoS Computational Biology},
  volume = {4},
  number = {11},
  pages = {e1000209},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1000209},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\44QTBTPI\\Kiebel et al_2008_PLoS Computational Biology_A Hierarchy of Time-Scales and the Brain.pdf}
}

@article{kiebel_2008a,
  title = {Dynamic Causal Modelling for {{EEG}} and {{MEG}}},
  author = {Kiebel, Stefan J. and Garrido, Marta I. and Moran, Rosalyn J. and Friston, Karl J.},
  year = {2008},
  month = jun,
  journal = {Cognitive Neurodynamics},
  volume = {2},
  number = {2},
  pages = {121--136},
  issn = {1871-4080, 1871-4099},
  doi = {10.1007/s11571-008-9038-0},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EMUUNG7K\\Kiebel et al_2008_Cognitive Neurodynamics_Dynamic causal modelling for EEG and MEG.pdf}
}

@article{kiebel_2008b,
  title = {Variational {{Bayesian}} Inversion of the Equivalent Current Dipole Model in {{EEG}}/{{MEG}}},
  author = {Kiebel, Stefan J. and Daunizeau, Jean and Phillips, Christophe and Friston, Karl J.},
  year = {2008},
  month = jan,
  journal = {NeuroImage},
  volume = {39},
  number = {2},
  pages = {728--741},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.09.005},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\32Z6Z8I2\\Kiebel et al_2008_NeuroImage_Variational Bayesian inversion of the equivalent current dipole model in EEG-MEG.pdf}
}

@article{kiebel_2009,
  title = {Perception and Hierarchical Dynamics},
  author = {Kiebel, Stefan J.},
  year = {2009},
  journal = {Frontiers in Neuroinformatics},
  volume = {3},
  issn = {16625196},
  doi = {10.3389/neuro.11.020.2009},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HX6XVIUU\\Kiebel_2009_Frontiers in Neuroinformatics_Perception and hierarchical dynamics.pdf}
}

@article{kiebel_2009a,
  title = {Recognizing {{Sequences}} of {{Sequences}}},
  author = {Kiebel, Stefan J. and {von Kriegstein}, Katharina and Daunizeau, Jean and Friston, Karl J.},
  editor = {K{\"o}tter, Rolf},
  year = {2009},
  month = aug,
  journal = {PLoS Computational Biology},
  volume = {5},
  number = {8},
  pages = {e1000464},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1000464},
  abstract = {The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of `stable heteroclinic channels'. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JGQQL4J3\\Kiebel et al_2009_PLoS Computational Biology_Recognizing Sequences of Sequences.pdf}
}

@article{kiefer_1953,
  title = {Sequential Minimax Search for a Maximum},
  author = {Kiefer, J.},
  year = {1953},
  month = mar,
  journal = {Proceedings of the American Mathematical Society},
  volume = {4},
  number = {3},
  pages = {502--502},
  issn = {0002-9939},
  doi = {10.1090/S0002-9939-1953-0055639-3},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\48PSF8RZ\\Kiefer - 1953 - Sequential minimax search for a maximum.pdf}
}

@article{kilner_2005,
  title = {Hemodynamic Correlates of {{EEG}}: {{A}} Heuristic},
  shorttitle = {Hemodynamic Correlates of {{EEG}}},
  author = {Kilner, J.M. and Mattout, J. and Henson, R. and Friston, K.J.},
  year = {2005},
  month = oct,
  journal = {NeuroImage},
  volume = {28},
  number = {1},
  pages = {280--286},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2005.06.008},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QGAMIRLP\\Kilner et al_2005_NeuroImage_Hemodynamic correlates of EEG.pdf}
}

@article{kilner_2010,
  title = {Topological Inference for {{EEG}} and {{MEG}}},
  author = {Kilner, James M. and Friston, Karl J.},
  year = {2010},
  month = sep,
  journal = {The Annals of Applied Statistics},
  volume = {4},
  number = {3},
  pages = {1272--1290},
  issn = {1932-6157, 1941-7330},
  abstract = {Neuroimaging produces data that are continuous in one or more dimensions. This calls for an inference framework that can handle data that approximate functions of space, for example, anatomical images, time\textendash frequency maps and distributed source reconstructions of electromagnetic recordings over time. Statistical parametric mapping (SPM) is the standard framework for whole-brain inference in neuroimaging: SPM uses random field theory to furnish p-values that are adjusted to control family-wise error or false discovery rates, when making topological inferences over large volumes of space. Random field theory regards data as realizations of a continuous process in one or more dimensions. This contrasts with classical approaches like the Bonferroni correction, which consider images as collections of discrete samples with no continuity properties (i.e., the probabilistic behavior at one point in the image does not depend on other points). Here, we illustrate how random field theory can be applied to data that vary as a function of time, space or frequency. We emphasize how topological inference of this sort is invariant to the geometry of the manifolds on which data are sampled. This is particularly useful in electromagnetic studies that often deal with very smooth data on scalp or cortical meshes. This application illustrates the versatility and simplicity of random field theory and the seminal contributions of Keith Worsley (1951\textendash 2009), a key architect of topological inference.},
  langid = {english},
  mrnumber = {MR2758328},
  zmnumber = {1202.62154},
  keywords = {Random field theory,statistical parametric mapping,topological inference},
  annotation = {00073},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GAZS65K9\\Kilner_Friston_2010_The Annals of Applied Statistics_Topological inference for EEG and MEG.pdf;C\:\\Users\\dirko\\Zotero\\storage\\HWJKP23I\\1287409373.html}
}

@article{kim_2004,
  title = {Improved Ballistocardiac Artifact Removal from the Electroencephalogram Recorded in {{fMRI}}},
  author = {Kim, Kyung Hwan and Yoon, Hyo Woon and Park, Hyun Wook},
  year = {2004},
  month = may,
  journal = {Journal of Neuroscience Methods},
  volume = {135},
  number = {1-2},
  pages = {193--203},
  issn = {01650270},
  doi = {10.1016/j.jneumeth.2003.12.016},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\39EVGMFP\\Kim et al_2004_Journal of Neuroscience Methods_Improved ballistocardiac artifact removal from the electroencephalogram.pdf}
}

@article{kim_2012,
  title = {Biophysical and {{Physiological Origins}} of {{Blood Oxygenation Level-Dependent fMRI Signals}}},
  author = {Kim, Seong-Gi and Ogawa, Seiji},
  year = {2012},
  month = jul,
  journal = {Journal of Cerebral Blood Flow \& Metabolism},
  volume = {32},
  number = {7},
  pages = {1188--1206},
  issn = {0271-678X, 1559-7016},
  doi = {10.1038/jcbfm.2012.23},
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}

@article{king_2013,
  title = {Single-Trial Decoding of Auditory Novelty Responses Facilitates the Detection of Residual Consciousness},
  author = {King, J.R. and Faugeras, F. and Gramfort, A. and Schurger, A. and El Karoui, I. and Sitt, J.D. and Rohaut, B. and Wacongne, C. and Labyt, E. and Bekinschtein, T. and Cohen, L. and Naccache, L. and Dehaene, S.},
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  journal = {NeuroImage},
  volume = {83},
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  issn = {10538119},
  doi = {10.1016/j.neuroimage.2013.07.013},
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}

@article{kingma_2013,
  title = {Auto-{{Encoding Variational Bayes}}},
  author = {Kingma, Diederik P. and Welling, Max},
  year = {2013},
  month = dec,
  journal = {arXiv:1312.6114 [cs, stat]},
  eprint = {1312.6114},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {04544},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CEEUQFGH\\Kingma und Welling - 2013 - Auto-Encoding Variational Bayes.pdf}
}

@article{kingma_2019,
  ids = {kingma_introduction_2019a},
  title = {An {{Introduction}} to {{Variational Autoencoders}}},
  author = {Kingma, Diederik P. and Welling, Max},
  year = {2019},
  journal = {Foundations and Trends\textregistered{} in Machine Learning},
  volume = {12},
  number = {4},
  eprint = {1906.02691},
  eprinttype = {arxiv},
  pages = {307--392},
  issn = {1935-8237, 1935-8245},
  doi = {10.1561/2200000056},
  abstract = {Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
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}

@article{kira_2015,
  title = {A {{Neural Implementation}} of {{Wald}}'s {{Sequential Probability Ratio Test}}},
  author = {Kira, Shinichiro and Yang, Tianming and Shadlen, Michael~N.},
  year = {2015},
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  journal = {Neuron},
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}

@article{kirilina_2016,
  title = {The Quest for the Best: {{The}} Impact of Different {{EPI}} Sequences on the Sensitivity of Random Effect {{fMRI}} Group Analyses},
  shorttitle = {The Quest for the Best},
  author = {Kirilina, Evgeniya and Lutti, Antoine and Poser, Benedikt A. and Blankenburg, Felix and Weiskopf, Nikolaus},
  year = {2016},
  month = feb,
  journal = {NeuroImage},
  volume = {126},
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  doi = {10.1016/j.neuroimage.2015.10.071},
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@article{kirlic_2017,
  title = {Animal to Human Translational Paradigms Relevant for Approach Avoidance Conflict Decision Making},
  author = {Kirlic, Namik and Young, Jared and Aupperle, Robin L.},
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  month = sep,
  journal = {Behaviour Research and Therapy},
  volume = {96},
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  doi = {10.1016/j.brat.2017.04.010},
  abstract = {Avoidance behavior in clinical anxiety disorders is often a decision made in response to approachavoidance conflict, resulting in a sacrifice of potential rewards to avoid potential negative affective consequences. Animal research has a long history of relying on paradigms related to approach-avoidance conflict to model anxiety-relevant behavior. This approach includes punishment-based conflict, exploratory, and social interaction tasks. There has been a recent surge of interest in the translation of paradigms from animal to human, in efforts to increase generalization of findings and support the development of more effective mental health treatments. This article briefly reviews animal tests related to approach-avoidance conflict and results from lesion and pharmacologic studies utilizing these tests. We then provide a description of translational human paradigms that have been developed to tap into related constructs, summarizing behavioral and neuroimaging findings. Similarities and differences in findings from analogous animal and human paradigms are discussed. Lastly, we highlight opportunities for future research and paradigm development that will support the clinical utility of this translational work.},
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@book{klein_2018,
  title = {{Einf\"uhrung in Python 3: f\"ur Ein- und Umsteiger}},
  shorttitle = {{Einf\"uhrung in Python 3}},
  author = {Klein, Bernd},
  year = {2018},
  edition = {3., \"uberarbeitete Auflage},
  publisher = {{Hanser}},
  address = {{M\"unchen}},
  isbn = {978-3-446-45208-4 978-3-446-45387-6},
  langid = {german},
  annotation = {00007  OCLC: 985980451},
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@article{klema_1980,
  title = {The Singular Value Decomposition: {{Its}} Computation and Some Applications},
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  author = {Klema, V. and Laub, A.},
  year = {1980},
  month = apr,
  journal = {IEEE Transactions on Automatic Control},
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@article{klibanoff_2003,
  title = {A {{Smooth Model}} of {{Decision Making}} under {{Ambiguity}}},
  author = {Klibanoff, Peter and Marinacci, Massimo and Mukerji, Sujoy},
  year = {2003},
  journal = {SSRN Electronic Journal},
  issn = {1556-5068},
  doi = {10.2139/ssrn.395600},
  abstract = {We propose and characterize a model of preferences over acts such that the decision maker prefers act f to act g if and only if E\textmu{$\varphi$}(E{$\pi$}u {$\smwhtcircle$} f ) {$\geq$} E\textmu{$\varphi$}(E{$\pi$}u {$\smwhtcircle$} g), where E is the expectation operator, u is a von Neumann\textendash Morgenstern utility function, {$\varphi$} is an increasing transformation, and \textmu{} is a subjective probability over the set {$\Pi$} of probability measures {$\pi$} that the decision maker thinks are relevant given his subjective information. A key feature of our model is that it achieves a separation between ambiguity, identified as a characteristic of the decision maker's subjective beliefs, and ambiguity attitude, a characteristic of the decision maker's tastes. We show that attitudes toward pure risk are characterized by the shape of u, as usual, while attitudes toward ambiguity are characterized by the shape of {$\varphi$} Ambiguity itself is defined behaviorally and is shown to be characterized by properties of the subjective set of measures {$\Pi$}. One advantage of this model is that the well-developed machinery for dealing with risk attitudes can be applied as well to ambiguity attitudes. The model is also distinct from many in the literature on ambiguity in that it allows smooth, rather than kinked, indifference curves. This leads to different behavior and improved tractability, while still sharing the main features (e.g., Ellsberg's paradox). The maxmin expected utility model (e.g., Gilboa and Schmeidler (1989)) with a given set of measures may be seen as a limiting case of our model with infinite ambiguity aversion. Two illustrative portfolio choice examples are offered.},
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}

@article{klingner_2015,
  title = {Parallel Processing of Somatosensory Information: {{Evidence}} from Dynamic Causal Modeling of {{MEG}} Data},
  shorttitle = {Parallel Processing of Somatosensory Information},
  author = {Klingner, Carsten M. and Brodoehl, Stefan and Huonker, Ralph and G{\"o}tz, Theresa and Baumann, Lydia and Witte, Otto W.},
  year = {2015},
  month = sep,
  journal = {NeuroImage},
  volume = {118},
  pages = {193--198},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2015.06.028},
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}

@article{klkitatemlbalning_,
  title = {Reinforcement {{Learning}}: {{A Survey}}},
  author = {{kLKitatemlbalning}, LMeisclhieaePlaLc and Moore, Andrew W},
  journal = {Reinforcement Learning},
  pages = {49},
  abstract = {This paper surveys the eld of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the eld and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but di ers considerably in the details and in the use of the word reinforcement." The paper discusses central issues of reinforcement learning, including trading o exploration and exploitation, establishing the foundations of the eld via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.},
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}

@book{knabner_2018,
  title = {{Lineare Algebra: Grundlagen und Anwendungen}},
  shorttitle = {{Lineare Algebra}},
  author = {Knabner, Peter and Barth, Wolf},
  year = {2018},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-662-55600-9},
  isbn = {978-3-662-55599-6 978-3-662-55600-9},
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}

@incollection{knosche_2013,
  title = {Jansen-{{Rit Model}}},
  booktitle = {Encyclopedia of {{Computational Neuroscience}}},
  author = {Kn{\"o}sche, Thomas},
  editor = {Jaeger, Dieter and Jung, Ranu},
  year = {2013},
  pages = {1--5},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4614-7320-6_65-1},
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}

@article{koch_2006,
  title = {Synchronization between {{Background Activity}} and {{Visually Evoked Potential Is Not Mirrored}} by {{Focal Hyperoxygenation}}: {{Implications}} for the {{Interpretation}} of {{Vascular Brain Imaging}}},
  shorttitle = {Synchronization between {{Background Activity}} and {{Visually Evoked Potential Is Not Mirrored}} by {{Focal Hyperoxygenation}}},
  author = {Koch, S. P.},
  year = {2006},
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  journal = {Journal of Neuroscience},
  volume = {26},
  number = {18},
  pages = {4940--4948},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3989-05.2006},
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}

@article{koch_2008,
  title = {Individual Alpha-Frequency Correlates with Amplitude of Visual Evoked Potential and Hemodynamic Response},
  author = {Koch, Stefan P. and Koendgen, Sophie and Bourayou, Riad and Steinbrink, Jens and Obrig, Hellmuth},
  year = {2008},
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  journal = {NeuroImage},
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@misc{koch_2017,
  title = {Datenschutzrechtliche {{Anforderungen}} an Die Medizinische {{Forschung}} Unter {{Beru}} Cksichtigung Der {{EU Datenschutz Grundverordnung}}},
  author = {Koch, Holger and Sch{\"u}tze, Bernd and Spyra, Gerald and Wefer, Marina},
  year = {2017},
  publisher = {{Deutsche Gesellschaft f\"ur Medizinische Informatik, Biometrie und Epidemiologie e. V. und Gesellschaft f\"ur Datenschutz und Datensicherheit e. V.}},
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}

@book{kochenderfer_2019,
  title = {Algorithms for Optimization},
  author = {Kochenderfer, Mykel J. and Wheeler, Tim A.},
  year = {2019},
  publisher = {{The MIT Press}},
  address = {{Cambridge, Massachusetts}},
  isbn = {978-0-262-03942-0},
  langid = {english},
  lccn = {QA9.58 .K65425 2019},
  keywords = {Algorithms,Problems; exercises; etc},
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@article{koechlin_2003,
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@article{koechlin_2007,
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  journal = {Trends in Cognitive Sciences},
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@article{koechlin_2007a,
  title = {Anterior {{Prefrontal Function}} and the {{Limits}} of {{Human Decision-Making}}},
  author = {Koechlin, E. and Hyafil, A.},
  year = {2007},
  month = oct,
  journal = {Science},
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@article{koenig_2002,
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@article{koepsell_2008,
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@book{koller_2009,
  title = {Probabilistic Graphical Models: Principles and Techniques},
  shorttitle = {Probabilistic Graphical Models},
  author = {Koller, Daphne and Friedman, Nir},
  year = {2009},
  series = {Adaptive Computation and Machine Learning},
  publisher = {{MIT Press}},
  address = {{Cambridge, MA}},
  isbn = {978-0-262-01319-2},
  langid = {english},
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@article{kolling_2018,
  title = {Prospection, {{Perseverance}}, and {{Insight}} in {{Sequential Behavior}}},
  author = {Kolling, Nils and Scholl, Jacqueline and Chekroud, Adam and Trier, Hailey A. and Rushworth, Matthew F.S.},
  year = {2018},
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  abstract = {Real-world decisions have benefits occurring only later and dependent on additional decisions taken in the interim. We investigated this in a novel decision-making task in humans (n = 76) while measuring brain activity with fMRI (n = 24). Modeling revealed that participants computed the prospective value of decisions: they planned their future behavior taking into account how their decisions might affect which states they would encounter and how they themselves might respond in these states. They considered their own likely future behavioral biases (e.g., failure to adapt to changes in prospective value) and avoided situations in which they might be prone to such biases. Three neural networks in adjacent medial frontal regions were linked to distinct components of prospective decision making: activity in dorsal anterior cingulate cortex, area 8 m/9, and perigenual anterior cingulate cortex reflected prospective value, anticipated changes in prospective value, and the degree to which prospective value influenced decisions.},
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@book{kolmogoroff_1933,
  title = {{Grundbegriffe der Wahrscheinlichkeitsrechnung}},
  author = {Kolmogoroff, A.},
  year = {1933},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
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@book{kolmogorov_1956,
  title = {Foundations of the {{Theory}} of {{Probability}}},
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@article{kolossa_2015,
  title = {A Computational Analysis of the Neural Bases of {{Bayesian}} Inference},
  author = {Kolossa, Antonio and Kopp, Bruno and Fingscheidt, Tim},
  year = {2015},
  month = feb,
  journal = {NeuroImage},
  volume = {106},
  pages = {222--237},
  issn = {10538119},
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@book{kolossa_2016,
  title = {Computational {{Modeling}} of {{Neural Activities}} for {{Statistical Inference}}},
  author = {Kolossa, Antonio},
  year = {2016},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-319-32285-8},
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@article{kolossa_2018,
  title = {Data Quality over Data Quantity in Computational Cognitive Neuroscience},
  author = {Kolossa, Antonio and Kopp, Bruno},
  year = {2018},
  month = may,
  journal = {NeuroImage},
  volume = {172},
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  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2018.01.005},
  abstract = {We analyzed factors that may hamper the advancement of computational cognitive neuroscience (CCN). These factors include a particular statistical mindset, which paves the way for the dominance of statistical power theory and a preoccupation with statistical replicability in the behavioral and neural sciences. Exclusive statistical concerns about sampling error occur at the cost of an inadequate representation of the problem of measurement error. We contrasted the manipulation of data quantity (sampling error, by varying the number of subjects) against the manipulation of data quality (measurement error, by varying the number of data per subject) in a simulated Bayesian model identifiability study. The results were clear-cut in showing that - across all levels of signal-to-noise ratios - varying the number of subjects was completely inconsequential, whereas the number of data per subject exerted massive effects on model identifiability. These results emphasize data quality over data quantity, and they call for the integration of statistics and measurement theory.},
  keywords = {Computational modeling,Functional brain imaging,Reliability,Replicability,Signal-to-noise ratio},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UDWBLRUN\\Kolossa_Kopp_2018_NeuroImage_Data quality over data quantity in computational cognitive neuroscience.pdf;C\:\\Users\\dirko\\Zotero\\storage\\U9YVE9UV\\S1053811918300053.html}
}

@book{konishi_2008,
  title = {Information Criteria and Statistical Modeling},
  author = {Konishi, Sadanori and Kitagawa, G.},
  year = {2008},
  series = {Springer Series in Statistics},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-71886-6 978-0-387-71887-3},
  langid = {english},
  lccn = {QA274.2 .K667 2008},
  keywords = {Information modeling,Mathematical analysis,Stochastic analysis},
  annotation = {00671},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZPP2HAPE\\Konishi and Kitagawa - 2008 - Information criteria and statistical modeling.pdf}
}

@article{konovalov_2018,
  title = {Neurocomputational {{Dynamics}} of {{Sequence Learning}}},
  author = {Konovalov, Arkady and Krajbich, Ian},
  year = {2018},
  journal = {Neuron},
  pages = {1--12},
  issn = {10974199},
  doi = {10.1016/j.neuron.2018.05.013},
  annotation = {00000}
}

@article{konstantopoulos_2011,
  title = {Fixed Effects and Variance Components Estimation in Three-Level Meta-Analysis: {{Three-level}} Meta-Analysis},
  shorttitle = {Fixed Effects and Variance Components Estimation in Three-Level Meta-Analysis},
  author = {Konstantopoulos, Spyros},
  year = {2011},
  month = mar,
  journal = {Research Synthesis Methods},
  volume = {2},
  number = {1},
  pages = {61--76},
  issn = {17592879},
  doi = {10.1002/jrsm.35},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\M3R9AFTZ\\Konstantopoulos - 2011 - Fixed effects and variance components estimation i.pdf}
}

@book{kopper_2005,
  title = {The {{Linux Enterprise Cluster}}: Build a Highly Available Cluster with Commodity Hardware and Free Software},
  shorttitle = {The {{Linux Enterprise Cluster}}},
  author = {Kopper, Karl},
  year = {2005},
  publisher = {{No Starch Press}},
  address = {{San Francisco}},
  isbn = {978-1-59327-036-0},
  langid = {english},
  lccn = {QA76.58 .K67 2005},
  keywords = {Cluster analysis,Distributed processing,Electronic data processing,Linux,Parallel processing (Electronic computers)},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RFI59IHU\\Kopper - 2005 - The Linux Enterprise Cluster build a highly avail.pdf}
}

@article{korf_,
  ids = {korf_real-time_1990},
  title = {Real-{{Time Heuristic Search}}},
  author = {Korf, Richard E},
  pages = {23},
  abstract = {We apply the two-player game assumptions of limited search horizon and commitment to moves in constant time, to single-agent heuristic search problems. We present a variation of minimax lookahead search, and an analog to alpha-beta pruning that significantly improves the efficiency of the algorithm. Paradoxically, the search horizon reachable with this algorithm increases with increasing branching factor. In addition, we present a new algorithm, called Real-Time-A \textasciitilde, for interleaving planning and execution. We prove that the algorithm makes locally optimal decisions and is guaranteed to find a solution. We also present a learning version of this algorithm that improves its performance over successive problem solving trials by learning more accurate heuristic values, and prove that the learned values converge to their exact values along every optimal path. These algorithms effectively solve significantly larger problems than have previously been solvable using heuristic evaluation functions.},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\53UM96TA\\Korf - Real-Time Heuristic Search.pdf}
}

@article{korn_2018,
  ids = {korn_heuristic_2018-1},
  title = {Heuristic and Optimal Policy Computations in the Human Brain during Sequential Decision-Making},
  author = {Korn, Christoph W. and Bach, Dominik R.},
  year = {2018},
  month = jan,
  journal = {Nature Communications},
  volume = {9},
  number = {1},
  pages = {325},
  issn = {2041-1723},
  doi = {10.1038/s41467-017-02750-3},
  abstract = {Alhough humans often make a series of related decisions, it is unknown whether this is done by relying on optimal or heuristic strategies. Here, the\&nbsp;authors show that humans rely on both\&nbsp;the best heuristic and the optimal policy, and that these strategies are controlled by parts of the medial prefrontal cortex.},
  copyright = {2018 The Author(s)},
  langid = {english},
  pmid = {29362449},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DQY3TDGU\\41467_2017_2750_MOESM1_ESM.pdf;C\:\\Users\\dirko\\Zotero\\storage\\KG4NFAQQ\\Korn_Bach_2018_Nature Communications_Heuristic and optimal policy computations in the human brain during sequential.pdf;C\:\\Users\\dirko\\Zotero\\storage\\T22SMQRF\\41467_2017_2750_MOESM2_ESM.pdf;C\:\\Users\\dirko\\Zotero\\storage\\437GPK6T\\s41467-017-02750-3.html}
}

@article{koskinen_2009,
  title = {Removal of Imaging Artifacts in {{EEG}} during Simultaneous {{EEG}}/{{fMRI}} Recording: {{Reconstruction}} of a High-Precision Artifact Template},
  shorttitle = {Removal of Imaging Artifacts in {{EEG}} during Simultaneous {{EEG}}/{{fMRI}} Recording},
  author = {Koskinen, Miika and Vartiainen, Nuutti},
  year = {2009},
  month = may,
  journal = {NeuroImage},
  volume = {46},
  number = {1},
  pages = {160--167},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.01.061},
  langid = {english},
  annotation = {00000},
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}

@article{kosslyn_1988,
  title = {Aspects of a Cognitive Neuroscience of Mental Imagery},
  author = {Kosslyn, S.},
  year = {1988},
  month = jun,
  journal = {Science},
  volume = {240},
  number = {4859},
  pages = {1621--1626},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.3289115},
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}

@article{kosslyn_1993,
  title = {Visual {{Mental Imagery Activates Topographically Organized Visual Cortex}}: {{PET Investigations}}},
  shorttitle = {Visual {{Mental Imagery Activates Topographically Organized Visual Cortex}}},
  author = {Kosslyn, Stephen M. and Alpert, Nathaniel M. and Thompson, William L. and Maljkovic, Vera and Weise, Steven B. and Chabris, Christopher F. and Hamilton, Sania E. and Rauch, Scott L. and Buonanno, Ferdinando S.},
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  journal = {Journal of Cognitive Neuroscience},
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}

@article{koul_2018,
  title = {{{PredPsych}}: {{A}} Toolbox for Predictive Machine Learning-Based Approach in Experimental Psychology Research},
  shorttitle = {{{PredPsych}}},
  author = {Koul, Atesh and Becchio, Cristina and Cavallo, Andrea},
  year = {2018},
  month = aug,
  journal = {Behavior Research Methods},
  volume = {50},
  number = {4},
  pages = {1657--1672},
  issn = {1554-3528},
  doi = {10.3758/s13428-017-0987-2},
  abstract = {Recent years have seen an increased interest in machine learning-based predictive methods for analyzing quantitative behavioral data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible implementation. The aim of current work was to build an open-source R toolbox \textendash{} BPredPsych\^ \textendash that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine-learning predictive algorithms. In this paper, we present the framework of PredPsych via the analysis of a recently published multiple-subject motion capture dataset. In addition, we discuss examples of possible research questions that can be addressed with the machine-learning algorithms implemented in PredPsych and cannot be easily addressed with univariate statistical analysis. We anticipate that PredPsych will be of use to researchers with limited programming experience not only in the field of psychology, but also in that of clinical neuroscience, enabling computational assessment of putative bio-behavioral markers for both prognosis and diagnosis.},
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}

@book{kowalczyk_2017,
  title = {Support {{Vector Machines Succinctly}}},
  author = {Kowalczyk, Alexandre},
  year = {2017},
  annotation = {00000},
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}

@article{krahn_,
  title = {{Datenschutzrechtliche Vorgaben f\"ur digitale Forschungsdaten}},
  author = {Krahn, Philipp},
  pages = {27},
  langid = {german},
  annotation = {00000},
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}

@book{krantz_1971,
  title = {Foundations of Measurement. 1: {{Additive}} and Polynomial Representations / {{David H}}. {{Krantz}}},
  shorttitle = {Foundations of Measurement. 1},
  editor = {Krantz, David H.},
  year = {1971},
  publisher = {{Dover}},
  address = {{Mineola, NY}},
  isbn = {978-0-486-45314-9},
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}

@article{kraskov_2004,
  title = {Estimating Mutual Information},
  author = {Kraskov, Alexander and St{\"o}gbauer, Harald and Grassberger, Peter},
  year = {2004},
  month = jun,
  journal = {Physical Review E},
  volume = {69},
  number = {6},
  issn = {1539-3755, 1550-2376},
  doi = {10.1103/PhysRevE.69.066138},
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  annotation = {00000},
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}

@article{kreher_2005,
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  author = {Kreher, B. W. and Schneider, J. F. and Mader, I. and Martin, E. and Hennig, J. and Il'yasov, K. A.},
  year = {2005},
  month = nov,
  journal = {Magnetic Resonance in Medicine},
  volume = {54},
  number = {5},
  pages = {1216--1225},
  issn = {0740-3194, 1522-2594},
  doi = {10.1002/mrm.20670},
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}

@article{kreher_2008,
  title = {Gibbs Tracking: {{A}} Novel Approach for the Reconstruction of Neuronal Pathways},
  shorttitle = {Gibbs Tracking},
  author = {Kreher, B.W. and Mader, I. and Kiselev, V.G.},
  year = {2008},
  month = oct,
  journal = {Magnetic Resonance in Medicine},
  volume = {60},
  number = {4},
  pages = {953--963},
  issn = {07403194, 15222594},
  doi = {10.1002/mrm.21749},
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}

@book{krengel_2005,
  title = {{Einf\"uhrung in die Wahrscheinlichkeitstheorie und Statistik}},
  author = {Krengel, Ulrich},
  year = {2005},
  publisher = {{Vieweg+Teubner Verlag}},
  address = {{Wiesbaden}},
  doi = {10.1007/978-3-663-09885-0},
  isbn = {978-3-8348-0063-3 978-3-663-09885-0},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YY285R2H\\Krengel - 2005 - Einführung in die Wahrscheinlichkeitstheorie und S.pdf}
}

@article{kreutzer_,
  title = {{Rechtsfragen bei Open Science. Ein Leitfaden}},
  author = {Kreutzer, Dr Till},
  pages = {156},
  langid = {german},
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}

@book{kreyszig_1998,
  title = {{Statistische Methoden und ihre Anwendungen}},
  author = {Kreyszig, Erwin},
  year = {1998},
  edition = {5., unver\"and. Nachdr. d. 7. Aufl},
  publisher = {{Vandenhoeck und Ruprecht}},
  address = {{G\"ottingen}},
  isbn = {978-3-525-40717-2},
  langid = {german},
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}

@book{kreyszig_2006,
  title = {Advanced Engineering Mathematics},
  author = {Kreyszig, Erwin},
  year = {2006},
  edition = {9th ed},
  publisher = {{John Wiley}},
  address = {{Hoboken, NJ}},
  isbn = {978-0-471-48885-9 978-0-471-72897-9 978-0-471-72644-9 978-0-471-72645-6 978-0-471-72646-3},
  lccn = {QA401 .K7 2006},
  keywords = {Engineering mathematics,Mathematical physics},
  annotation = {00000  OCLC: ocm57349166},
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}

@book{krickeberg_1995,
  title = {{Stochastische Methoden}},
  author = {Krickeberg, Klaus and Ziezold, Herbert},
  year = {1995},
  series = {{Springer-Lehrbuch}},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-642-57862-5},
  isbn = {978-3-540-57792-8 978-3-642-57862-5},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VXD97X2S\\Krickeberg und Ziezold - 1995 - Stochastische Methoden.pdf}
}

@article{kriegeskorte_2007,
  title = {Individual Faces Elicit Distinct Response Patterns in Human Anterior Temporal Cortex},
  author = {Kriegeskorte, N. and Formisano, E. and Sorger, B. and Goebel, R.},
  year = {2007},
  month = dec,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {104},
  number = {51},
  pages = {20600--20605},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0705654104},
  langid = {english},
  annotation = {00000},
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}

@article{kriegeskorte_2008,
  title = {Representational Similarity Analysis \textendash{} Connecting the Branches of Systems Neuroscience},
  author = {Kriegeskorte, Nikolaus},
  year = {2008},
  journal = {Frontiers in Systems Neuroscience},
  issn = {16625137},
  doi = {10.3389/neuro.06.004.2008},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TPPQC9R8\\Kriegeskorte_2008_Frontiers in Systems Neuroscience_Representational similarity analysis – connecting the branches of systems.pdf}
}

@article{kriegeskorte_2009,
  title = {Circular Analysis in Systems Neuroscience: The Dangers of Double Dipping},
  shorttitle = {Circular Analysis in Systems Neuroscience},
  author = {Kriegeskorte, Nikolaus and Simmons, W Kyle and Bellgowan, Patrick S F and Baker, Chris I},
  year = {2009},
  month = may,
  journal = {Nature Neuroscience},
  volume = {12},
  number = {5},
  pages = {535--540},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.2303},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HQKZI653\\Kriegeskorte et al_2009_Nature Neuroscience_Circular analysis in systems neuroscience.pdf}
}

@article{kriegeskorte_2011,
  title = {Pattern-Information Analysis: {{From}} Stimulus Decoding to Computational-Model Testing},
  shorttitle = {Pattern-Information Analysis},
  author = {Kriegeskorte, Nikolaus},
  year = {2011},
  month = may,
  journal = {NeuroImage},
  volume = {56},
  number = {2},
  pages = {411--421},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2011.01.061},
  langid = {english},
  annotation = {00000},
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}

@article{kriegeskorte_2018,
  title = {Cognitive Computational Neuroscience},
  author = {Kriegeskorte, Nikolaus and Douglas, Pamela K.},
  year = {2018},
  month = aug,
  journal = {Nature Neuroscience},
  volume = {21},
  pages = {1148--1160},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-018-0210-5},
  langid = {english},
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}

@book{krishnamurthy_2016,
  title = {Partially Observed {{Markov}} Decision Processes: From Filtering to Controlled Sensing},
  shorttitle = {Partially Observed {{Markov}} Decision Processes},
  author = {Krishnamurthy, V.},
  year = {2016},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge}},
  isbn = {978-1-107-13460-7},
  langid = {english},
  lccn = {QA274.7 .K75 2016},
  keywords = {Markov processes,Stochastic processes,Textbooks},
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}

@article{krohn_2017,
  title = {Computing Integrated Information},
  author = {Krohn, Stephan and Ostwald, Dirk},
  year = {2017},
  month = jan,
  journal = {Neuroscience of Consciousness},
  volume = {2017},
  number = {1},
  issn = {2057-2107},
  doi = {10.1093/nc/nix017},
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}

@article{kruglanski_2020,
  title = {All {{Thinking}} Is `{{Wishful}}' {{Thinking}}},
  author = {Kruglanski, Arie W. and Jasko, Katarzyna and Friston, Karl},
  year = {2020},
  month = apr,
  journal = {Trends in Cognitive Sciences},
  pages = {S1364661320300796},
  issn = {13646613},
  doi = {10.1016/j.tics.2020.03.004},
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}

@article{kruskal_,
  title = {Helmert's {{Distribution}}},
  author = {Kruskal, William},
  pages = {5},
  langid = {english},
  annotation = {00039},
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}

@article{kuhn_1962,
  title = {The {{Structure}} of {{Scientific Revolutions}}},
  author = {Kuhn, Thomas S},
  year = {1962},
  number = {2},
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}

@book{kuhn_2013,
  title = {Applied {{Predictive Modeling}}},
  author = {Kuhn, Max and Johnson, Kjell},
  year = {2013},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4614-6849-3},
  isbn = {978-1-4614-6848-6 978-1-4614-6849-3},
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}

@article{kuhner_2007,
  title = {{Reliabilit\"at und Validit\"at des revidierten Beck-Depressionsinventars (BDI-II): Befunde aus deutschsprachigen Stichproben}},
  shorttitle = {{Reliabilit\"at und Validit\"at des revidierten Beck-Depressionsinventars (BDI-II)}},
  author = {K{\"u}hner, C. and B{\"u}rger, C. and Keller, F. and Hautzinger, M.},
  year = {2007},
  month = jun,
  journal = {Der Nervenarzt},
  volume = {78},
  number = {6},
  pages = {651--656},
  issn = {0028-2804, 1433-0407},
  doi = {10.1007/s00115-006-2098-7},
  abstract = {Background .\hspace{0.6em} The Beck Depression Inventory (BDI) underwent revision in 1996 (BDI-II) with the goal of addressing DSM-IV depression criteria. The present study assessed psychometric properties of the German version of the BDI-II. Patients and methods.\hspace{0.6em} The BDI-II was translated into German and evaluated in a series of studies with clinical and nonclinical samples.},
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}

@article{kullback_1951,
  ids = {kullback1951},
  title = {On {{Information}} and {{Sufficiency}}},
  author = {Kullback, S. and Leibler, R. A.},
  year = {1951},
  month = mar,
  journal = {The Annals of Mathematical Statistics},
  volume = {22},
  number = {1},
  pages = {79--86},
  issn = {0003-4851},
  doi = {10.1214/aoms/1177729694},
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}

@book{kullback_1987,
  title = {Topics in {{Statistical Information Theory}}},
  author = {Kullback, Solomon and Keegel, John C. and Kullback, Joseph H.},
  editor = {Brillinger, D. and Fienberg, S. and Gani, J. and Hartigan, J. and Krickeberg, K.},
  year = {1987},
  series = {Lecture {{Notes}} in {{Statistics}}},
  volume = {42},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4615-8080-5},
  isbn = {978-0-387-96512-3 978-1-4615-8080-5},
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@book{kuncheva_2004,
  title = {Combining Pattern Classifiers: Methods and Algorithms},
  shorttitle = {Combining Pattern Classifiers},
  author = {Kuncheva, Ludmila I.},
  year = {2004},
  publisher = {{J. Wiley}},
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@article{kurth-nelson_2016,
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@article{kutas_2011,
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@article{kutschireiter_2020,
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  abstract = {Nonlinear filtering is used in online estimation of a dynamic hidden variable from incoming data and has vast applications in different fields, ranging from engineering, machine learning, economic science and natural sciences. We start our review of the theory on nonlinear filtering from the simplest `filtering' task we can think of, namely static Bayesian inference. From there we continue our journey through discrete-time models, which are usually encountered in machine learning, and generalize to continuous-time filtering theory. The idea of changing the probability measure connects and elucidates several aspects of the theory, such as the parallels between the discrete- and continuous-time problems and between different observation models. Furthermore, it provides insight into the construction of particle filtering algorithms. This tutorial is targeted at scientists and engineers and should serve as an introduction to the main ideas of nonlinear filtering, and as a segway to more advanced and specialized literature.},
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  abstract = {Neuronal activity causes local changes in cerebral blood flow, blood volume, and blood oxygenation. Magnetic resonance imaging (MRI) techniques sensitive to changes in cerebral blood flow and blood oxygenation were developed by high-speed echo planar imaging. These techniques were used to obtain completely noninvasive tomographic maps of human brain activity, by using visual and motor stimulus paradigms. Changes in blood oxygenation were detected by using a gradient echo (GE) imaging sequence sensitive to the paramagnetic state of deoxygenated hemoglobin. Blood flow changes were evaluated by a spin-echo inversion recovery (IR), tissue relaxation parameter Tl-sensitive pulse sequence. A series of images were acquired continuously with the same imaging pulse sequence (either GE or IR) during task activation. Cine display of subtraction images (activated minus baseline) directly demonstrates activity-induced changes in brain MR signal observed at a temporal resolution of seconds. During 8-Hz patterned-flash photic stimulation, a significant increase in signal intensity (paired t test; P {$<$} 0.001) of 1.8\% {$\pm$} 0.8\% (GE) and 1.8\% {$\pm$} 0.9\% (ID) was observed in the primary visual cortex (Vi) of seven normal volunteers. The mean rise-time constant of the signal change was 4.4 {$\pm$} 2.2 s for the GE images and 8.9 {$\pm$} 2.8 s for the IR images. The stimulation frequency dependence of visual activation agrees with previous positron emission tomography observations, with the largest MR signal response occurring at 8 Hz. Similar signal changes were observed within the human primary motor cortex (Ml) during a hand squeezing task and in animal models of increased blood flow by hypercapnia. By using intrinsic blood-tissue contrast, functional MRI opens a spatialtemporal window onto individual brain physiology.},
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@article{lak_2017,
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@article{laquitaine_2018,
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@book{lattimore_2020,
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@misc{lauritzen_2009,
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@article{lawley_2021,
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  lccn = {RC386.6.M34 L39 2008},
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@article{lee_2008,
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  address = {{Chichester, West Sussex ; Hoboken, N.J}},
  abstract = {"- Presents extensive examples throughout the book to complement the theory presented.Includes significant new material on recent techniques such as variational methods, importance sampling, approximate computation and reversible jump MCMC"--},
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@article{lee_2013,
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@article{lee_2019,
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  author = {Lee, Michael D. and Criss, Amy H. and Devezer, Berna and Donkin, Christopher and Etz, Alexander and Leite, F{\'a}bio P. and Matzke, Dora and Rouder, Jeffrey N. and Trueblood, Jennifer S. and White, Corey N. and Vandekerckhove, Joachim},
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@book{lehmann_2005,
  title = {Testing Statistical Hypotheses},
  author = {Lehmann, E. L. and Romano, Joseph P.},
  year = {2005},
  series = {Springer Texts in Statistics},
  edition = {3rd ed},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-98864-1},
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@article{lehmann_2019,
  title = {One-Shot Learning and Behavioral Eligibility Traces in Sequential Decision Making},
  author = {Lehmann, Marco P and Xu, He A and Liakoni, Vasiliki and Herzog, Michael H and Gerstner, Wulfram and Preuschoff, Kerstin},
  year = {2019},
  month = nov,
  journal = {eLife},
  volume = {8},
  pages = {e47463},
  issn = {2050-084X},
  doi = {10.7554/eLife.47463},
  abstract = {In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms solving this credit assignment problem: In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one-shot). Here, we show one-shot learning of sequences. We developed a novel paradigm to directly observe which actions and states along a multi-step sequence are reinforced after a single reward. By focusing our analysis on those states for which RL with and without eligibility trace make qualitatively distinct predictions, we find direct behavioral (choice probability) and physiological (pupil dilation) signatures of reinforcement learning with eligibility trace across multiple sensory modalities.},
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}

@book{Lehmann1986,
  title = {Testing Statistical Hypotheses},
  author = {Lehmann, Erich L},
  year = {1986},
  publisher = {{Wiley Series in Probability and Statistics}}
}

@book{Lehmann2006,
  title = {Testing Statistical Hypotheses},
  author = {Lehmann, Erich L and Romano, Joseph P},
  year = {2006},
  publisher = {{Springer Science \& Business Media}}
}

@article{lehtonen_,
  title = {Practical {{Methods}} for {{Design}} and {{Analysis}} of {{Complex Surveys}}},
  author = {Lehtonen, Risto and Pahkinen, Erkki},
  pages = {362},
  langid = {english},
  annotation = {00585},
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}

@article{lei_2011,
  title = {Multimodal {{Functional Network Connectivity}}: {{An EEG-fMRI Fusion}} in {{Network Space}}},
  shorttitle = {Multimodal {{Functional Network Connectivity}}},
  author = {Lei, Xu and Ostwald, Dirk and Hu, Jiehui and Qiu, Chuan and Porcaro, Camillo and Bagshaw, Andrew P. and Yao, Dezhong},
  editor = {{Valdes-Sosa}, Pedro Antonio},
  year = {2011},
  month = sep,
  journal = {PLoS ONE},
  volume = {6},
  number = {9},
  pages = {e24642},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0024642},
  abstract = {EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using networkbased source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.},
  langid = {english},
  annotation = {00045},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\K4RSD3X5\\Lei et al. - 2011 - Multimodal Functional Network Connectivity An EEG.pdf}
}

@book{leithold_1976,
  title = {The Calculus, with Analytic Geometry},
  author = {Leithold, Louis},
  year = {1976},
  edition = {3d ed},
  publisher = {{Harper \& Row}},
  address = {{New York}},
  isbn = {978-0-06-043951-4},
  lccn = {QA303 .L428 1976b},
  keywords = {Calculus,Geometry; Analytic},
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}

@article{lemarechal_2018,
  title = {Comparison of Two Integration Methods for Dynamic Causal Modeling of Electrophysiological Data},
  author = {Lemar{\'e}chal, Jean-Didier and George, Nathalie and David, Olivier},
  year = {2018},
  month = jun,
  journal = {NeuroImage},
  volume = {173},
  pages = {623--631},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2018.02.031},
  abstract = {Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain regions. Based on a set of observations and a biophysical model of brain interactions, DCM uses a Bayesian framework to estimate the posterior distribution of the free parameters of the model (e.g. modulation of connectivity) and infer architectural properties of the most plausible model (i.e. model selection). When modeling electrophysiological event-related responses, the estimation of the model relies on the integration of the system of delay differential equations (DDEs) that describe the dynamics of the system. In this technical note, we compared two numerical schemes for the integration of DDEs. The first, and standard, scheme approximates the DDEs (more precisely, the state of the system, with respect to conduction delays among brain regions) using ordinary differential equations (ODEs) and solves it with a fixed step size. The second scheme uses a dedicated DDEs solver with adaptive step sizes to control error, making it theoretically more accurate. To highlight the effects of the approximation used by the first integration scheme in regard to parameter estimation and Bayesian model selection, we performed simulations of local field potentials using first, a simple model comprising 2 regions and second, a more complex model comprising 6 regions. In these simulations, the second integration scheme served as the standard to which the first one was compared. Then, the performances of the two integration schemes were directly compared by fitting a public mismatch negativity EEG dataset with different models. The simulations revealed that the use of the standard DCM integration scheme was acceptable for Bayesian model selection but underestimated the connectivity parameters and did not allow an accurate estimation of conduction delays. Fitting to empirical data showed that the models systematically obtained an increased accuracy when using the second integration scheme. We conclude that inference on connectivity strength and delay based on DCM for EEG/MEG requires an accurate integration scheme.},
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}

@article{lennert_2011,
  title = {Attentional {{Modulation}} of {{Neuromagnetic Evoked Responses}} in {{Early Human Visual Cortex}} and {{Parietal Lobe}} Following a {{Rank-Order Rule}}},
  author = {Lennert, T. and Cipriani, R. and Jolicoeur, P. and Cheyne, D. and {Martinez-Trujillo}, J. C.},
  year = {2011},
  month = nov,
  journal = {Journal of Neuroscience},
  volume = {31},
  number = {48},
  pages = {17622--17636},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.4781-11.2011},
  langid = {english},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\ICGKVPA5\\Lennert et al_2011_Journal of Neuroscience_Attentional Modulation of Neuromagnetic Evoked Responses in Early Human Visual.pdf;C\:\\Users\\dirko\\Zotero\\storage\\IHWCZH2A\\Lennert et al_2011_Journal of Neuroscience_Attentional Modulation of Neuromagnetic Evoked Responses in Early Human Visual.pdf}
}

@article{leshno_1993,
  title = {Multilayer Feedforward Networks with a Nonpolynomial Activation Function Can Approximate Any Function},
  author = {Leshno, Moshe and Lin, Vladimir Ya. and Pinkus, Allan and Schocken, Shimon},
  year = {1993},
  month = jan,
  journal = {Neural Networks},
  volume = {6},
  number = {6},
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  abstract = {Several researchers characterized the activation fimction under which multilayer feedforward networks can act as universal approximators. We show that most of all the characterizations that were reported thus far in the literature are special cases of the following general result: A standard multilayer feedforward network with a locally bounded piecewise continuous activation fimction can approximate an3, continuous function to any degree of accuracy if and only if the network's activation function is not a polynomial. We also emphasize the important role o f the threshold, asserting that without it the last theorem does not hold.},
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}

@book{lew_2007,
  title = {Dynamic Programming: A Computational Tool},
  shorttitle = {Dynamic Programming},
  author = {Lew, Art and Mauch, Holger},
  year = {2007},
  series = {Studies in Computational Intelligence},
  number = {v. 38},
  publisher = {{Springer}},
  address = {{Berlin ; New York}},
  isbn = {978-3-540-37013-0},
  lccn = {QA402.5 .L515 2007},
  keywords = {Dynamic programming},
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}

@book{lewandowsky_2011,
  title = {Computational Modeling in Cognition: Principles and Practice},
  shorttitle = {Computational Modeling in Cognition},
  author = {Lewandowsky, Stephan and Farrell, Simon},
  year = {2011},
  publisher = {{Sage Publications}},
  address = {{Thousand Oaks}},
  isbn = {978-1-4129-7076-1},
  lccn = {BF311 .L467 2011},
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}

@inproceedings{lewis_2017,
  title = {Integrating the {{Management}} of {{Personal Data Protection}} and {{Open Science}} with {{Research Ethics}}},
  author = {Lewis, Dave and Moorkens, Joss and Fatema, Kaniz},
  year = {2017},
  pages = {60--65},
  publisher = {{Association for Computational Linguistics}},
  doi = {10.18653/v1/W17-1607},
  abstract = {This paper examines the impact of the EU General Data Protection Regulation, in the context of the requirement from many research funders to provide open access research data, on current practices in Language Technology Research. We analyse the challenges that arise and the opportunities to address many of them through the use of existing open data practices for sharing language research data. We discuss the impact of this also on current practice in academic and industrial research ethics.},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\65U9PFBA\\Lewis et al. - 2017 - Integrating the Management of Personal Data Protec.pdf;C\:\\Users\\dirko\\Zotero\\storage\\FGHYDI4R\\Lewis et al. - 2017 - Integrating the Management of Personal Data Protec.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UN53QN9V\\Lewis et al. - 2017 - Integrating the Management of Personal Data Protec.pdf}
}

@article{li_2007,
  title = {Flexible {{Coding}} for {{Categorical Decisions}} in the {{Human Brain}}},
  author = {Li, S. and Ostwald, D. and Giese, M. and Kourtzi, Z.},
  year = {2007},
  month = nov,
  journal = {Journal of Neuroscience},
  volume = {27},
  number = {45},
  pages = {12321--12330},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3795-07.2007},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\GTZT4RDG\\Li et al_2007_Journal of Neuroscience_Flexible Coding for Categorical Decisions in the Human Brain.pdf}
}

@article{li_2011,
  title = {Generalised Filtering and Stochastic {{DCM}} for {{fMRI}}},
  author = {Li, Baojuan and Daunizeau, Jean and Stephan, Klaas E. and Penny, Will and Hu, Dewen and Friston, Karl},
  year = {2011},
  month = sep,
  journal = {NeuroImage},
  volume = {58},
  number = {2},
  pages = {442--457},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2011.01.085},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\M5HRYN3X\\Li et al_2011_NeuroImage_Generalised filtering and stochastic DCM for fMRI.pdf}
}

@article{li_2015,
  title = {A Voxelation-Corrected Non-Stationary {{3D}} Cluster-Size Test Based on Random Field Theory},
  author = {Li, Huanjie and Nickerson, Lisa D. and Zhao, Xuna and Nichols, Thomas E. and Gao, Jia-Hong},
  year = {2015},
  month = sep,
  journal = {NeuroImage},
  volume = {118},
  pages = {676--682},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2015.05.094},
  abstract = {Cluster-size tests (CSTs) based on random field theory (RFT) are commonly adopted to identify significant differences in brain images. However, the use of RFT in CSTs rests on the assumption of uniform smoothness (stationarity). When images are non-stationary, CSTs based on RFT will likely lead to increased false positives in smooth regions and reduced power in rough regions. An adjustment to the cluster size according to the local smoothness at each voxel has been proposed for the standard test based on RFT to address non-stationarity, however, this technique requires images with a large degree of spatial smoothing, large degrees of freedom and high intensity thresholding. Recently, we proposed a voxelation-corrected 3D CST based on Gaussian random field theory that does not place constraints on the degree of spatial smoothness. However, this approach is only applicable to stationary images, requiring further modification to enable use for non-stationary images. In this study, we present modifications of this method to develop a voxelation-corrected non-stationary 3D CST based on RFT. Both simulated and real data were used to compare the voxelation-corrected non-stationary CST to the standard clustersize adjusted non-stationary CST based on RFT and the voxelation-corrected stationary CST. We found that voxelation-corrected stationary CST is liberal for non-stationary images and the voxelation-corrected nonstationary CST performs better than cluster-size adjusted non-stationary CST based on RFT under low smoothness, low intensity threshold and low degrees of freedom.},
  langid = {english},
  annotation = {00003},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HI3XHJ2C\\Li et al. - 2015 - A voxelation-corrected non-stationary 3D cluster-s.pdf}
}

@article{lieder_2013,
  title = {Modelling {{Trial-by-Trial Changes}} in the {{Mismatch Negativity}}},
  author = {Lieder, Falk and Daunizeau, Jean and Garrido, Marta I. and Friston, Karl J. and Stephan, Klaas E.},
  editor = {Sporns, Olaf},
  year = {2013},
  month = feb,
  journal = {PLoS Computational Biology},
  volume = {9},
  number = {2},
  pages = {e1002911},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1002911},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4C7RFBQ2\\Lieder et al_2013_PLoS Computational Biology_Modelling Trial-by-Trial Changes in the Mismatch Negativity.pdf;C\:\\Users\\dirko\\Zotero\\storage\\MZFDBRR6\\Lieder et al_2013_PLoS Computational Biology_Modelling Trial-by-Trial Changes in the Mismatch Negativity.pdf}
}

@article{lighthall_2014,
  title = {Functional {{Compensation}} in the {{Ventromedial Prefrontal Cortex Improves Memory-Dependent Decisions}} in {{Older Adults}}},
  author = {Lighthall, N. R. and Huettel, S. A. and Cabeza, R.},
  year = {2014},
  month = nov,
  journal = {Journal of Neuroscience},
  volume = {34},
  number = {47},
  pages = {15648--15657},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.2888-14.2014},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DW396IZB\\Lighthall et al_2014_Journal of Neuroscience_Functional Compensation in the Ventromedial Prefrontal Cortex Improves.pdf}
}

@article{lillicrap_2020,
  title = {Backpropagation and the Brain},
  author = {Lillicrap, Timothy P. and Santoro, Adam and Marris, Luke and Akerman, Colin J. and Hinton, Geoffrey},
  year = {2020},
  month = apr,
  journal = {Nature Reviews Neuroscience},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/s41583-020-0277-3},
  abstract = {During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in deep artificial neural networks, but historically it has been viewed as biologically problematic. Nonetheless, recent developments in neuroscience and the successes of artificial neural networks have reinvigorated interest in whether backpropagation offers insights for understanding learning in the cortex. The backpropagation algorithm learns quickly by computing synaptic updates using feedback connections to deliver error signals. Although feedback connections are ubiquitous in the cortex, it is difficult to see how they could deliver the error signals required by strict formulations of backpropagation. Here we build on past and recent developments to argue that feedback connections may instead induce neural activities whose differences can be used to locally approximate these signals and hence drive effective learning in deep networks in the brain.},
  langid = {english},
  annotation = {00033},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\X2IWHNAX\\Lillicrap et al. - 2020 - Backpropagation and the brain.pdf}
}

@article{lin_2002,
  title = {Functional Characterization of Human Second Somatosensory Cortex by Magnetoencephalography},
  author = {Lin, Y.Y and Forss, N},
  year = {2002},
  month = sep,
  journal = {Behavioural Brain Research},
  volume = {135},
  number = {1-2},
  pages = {141--145},
  issn = {01664328},
  doi = {10.1016/S0166-4328(02)00143-2},
  abstract = {Magnetoencephalographic (MEG) recordings allow noninvasive monitoring of simultaneously active brain areas with reasonable spatial and excellent temporal resolution. Whole-scalp neuromagnetic recordings show activation of contralateral primary (SI) and bilateral second (SII) somatosensory cortices to unilateral median nerve stimulation. Recent MEG studies on healthy and diseased human subjects have shown some functional characteristics of SII cortex. Besides tactile input, the SII cortex also responds to nociceptive afferents. The SII activation is differentially modulated by isometric muscle contraction of various body parts. Lesions in the SII cortex may disturb the self-perception of body scheme. Moreover, the SI and SII cortices may be sequentially activated within one hemisphere, but the SII cortex may also receive direct peripheral input on the ipsilateral side. \# 2002 Elsevier Science B.V. All rights reserved.},
  langid = {english},
  annotation = {00088},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9NNX7S88\\Lin and Forss - 2002 - Functional characterization of human second somato.pdf}
}

@article{linden_2005,
  title = {The {{P300}}: {{Where}} in the {{Brain Is It Produced}} and {{What Does It Tell Us}}?},
  shorttitle = {The {{P300}}},
  author = {Linden, David E. J.},
  year = {2005},
  month = dec,
  journal = {The Neuroscientist},
  volume = {11},
  number = {6},
  pages = {563--576},
  issn = {1073-8584, 1089-4098},
  doi = {10.1177/1073858405280524},
  langid = {english},
  annotation = {00000},
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}

@article{lindley_1965,
  title = {Sequential {{Sampling}}: {{Two Decision Problems}} with {{Linear Losses}} for {{Binomial}} and {{Normal Random Variables}}},
  shorttitle = {Sequential {{Sampling}}},
  author = {Lindley, D. V. and Barnett, B. N.},
  year = {1965},
  month = dec,
  journal = {Biometrika},
  volume = {52},
  number = {3/4},
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  issn = {00063444},
  doi = {10.2307/2333702},
  annotation = {00067},
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}

@article{lindley_1993,
  title = {The {{Analysis}} of {{Experimental Data}}: {{The Appreciation}} of {{Tea}} and {{Wine}}},
  shorttitle = {The {{Analysis}} of {{Experimental Data}}},
  author = {Lindley, Dennis V},
  year = {1993},
  month = mar,
  journal = {Teaching Statistics},
  volume = {15},
  number = {1},
  pages = {22--25},
  issn = {0141-982X, 1467-9639},
  doi = {10.1111/j.1467-9639.1993.tb00252.x},
  abstract = {A classical experiment on the tasting of tea is used to show that many standard methods of analysis of the resulting data are unsatisfactory. A similar experiment with wine is used to show how a more sensible method may be developed.},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\2U882H68\\Lindley - 1993 - The Analysis of Experimental Data The Appreciatio.pdf}
}

@article{lindquist_2013,
  title = {Ironing out the Statistical Wrinkles in ``Ten Ironic Rules''},
  author = {Lindquist, Martin A. and Caffo, Brian and Crainiceanu, Ciprian},
  year = {2013},
  month = nov,
  journal = {NeuroImage},
  volume = {81},
  pages = {499--502},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2013.02.056},
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  annotation = {00000},
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}

@article{lipsey_,
  title = {Practical {{Meta-analysis}}},
  author = {Lipsey, Mark W and Wilson, David B},
  pages = {265},
  langid = {english},
  annotation = {09318},
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}

@article{little_2000,
  title = {Causal {{Effects}} in {{Clinical}} and {{Epidemiological Studies Via Potential Outcomes}}: {{Concepts}} and {{Analytical Approaches}}},
  shorttitle = {Causal {{Effects}} in {{Clinical}} and {{Epidemiological Studies Via Potential Outcomes}}},
  author = {Little, Roderick J. and Rubin, Donald B.},
  year = {2000},
  month = may,
  journal = {Annual Review of Public Health},
  volume = {21},
  number = {1},
  pages = {121--145},
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}

@article{littman_2009,
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  title = {A Tutorial on Partially Observable {{Markov}} Decision Processes},
  author = {Littman, Michael L.},
  year = {2009},
  month = jun,
  journal = {Journal of Mathematical Psychology},
  volume = {53},
  number = {3},
  pages = {119--125},
  publisher = {{Elsevier}},
  issn = {00222496},
  doi = {10.1016/j.jmp.2009.01.005},
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}

@article{litvak_2007,
  title = {Artifact Correction and Source Analysis of Early Electroencephalographic Responses Evoked by Transcranial Magnetic Stimulation over Primary Motor Cortex},
  author = {Litvak, Vladimir and Komssi, Soile and Scherg, Michael and Hoechstetter, Karsten and Classen, Joseph and Zaaroor, Menashe and Pratt, Hillel and Kahkonen, Seppo},
  year = {2007},
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  journal = {NeuroImage},
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  number = {1},
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  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.05.015},
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}

@article{litvak_2008,
  title = {Electromagnetic Source Reconstruction for Group Studies},
  author = {Litvak, Vladimir and Friston, Karl},
  year = {2008},
  month = oct,
  journal = {NeuroImage},
  volume = {42},
  number = {4},
  pages = {1490--1498},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.06.022},
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}

@article{litvak_2011,
  title = {{{EEG}} and {{MEG Data Analysis}} in {{SPM8}}},
  author = {Litvak, Vladimir and Mattout, J{\'e}r{\'e}mie and Kiebel, Stefan and Phillips, Christophe and Henson, Richard and Kilner, James and Barnes, Gareth and Oostenveld, Robert and Daunizeau, Jean and Flandin, Guillaume and Penny, Will and Friston, Karl},
  year = {2011},
  journal = {Computational Intelligence and Neuroscience},
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}

@article{litvak_2015,
  title = {Empirical {{Bayes}} for {{Group}} ({{DCM}}) {{Studies}}: {{A Reproducibility Study}}},
  shorttitle = {Empirical {{Bayes}} for {{Group}} ({{DCM}}) {{Studies}}},
  author = {Litvak, Vladimir and Garrido, Marta and Zeidman, Peter and Friston, Karl},
  year = {2015},
  month = dec,
  journal = {Frontiers in Human Neuroscience},
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}

@article{liu_2001,
  title = {Detection {{Power}}, {{Estimation Efficiency}}, and {{Predictability}} in {{Event-Related fMRI}}},
  author = {Liu, Thomas T. and Frank, Lawrence R. and Wong, Eric C. and Buxton, Richard B.},
  year = {2001},
  month = apr,
  journal = {NeuroImage},
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  doi = {10.1006/nimg.2000.0728},
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}

@article{liu_2004,
  title = {Efficiency, Power, and Entropy in Event-Related {{fMRI}} with Multiple Trial Types},
  author = {Liu, Thomas T.},
  year = {2004},
  month = jan,
  journal = {NeuroImage},
  volume = {21},
  number = {1},
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  issn = {10538119},
  doi = {10.1016/j.neuroimage.2003.09.031},
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}

@article{liu_2004a,
  title = {Efficiency, Power, and Entropy in Event-Related {{FMRI}} with Multiple Trial Types},
  author = {Liu, Thomas T. and Frank, Lawrence R.},
  year = {2004},
  month = jan,
  journal = {NeuroImage},
  volume = {21},
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  issn = {10538119},
  doi = {10.1016/j.neuroimage.2003.09.030},
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}

@article{liu_2012,
  title = {The Development of Event-Related {{fMRI}} Designs},
  author = {Liu, Thomas T.},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {2},
  pages = {1157--1162},
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}

@article{liu_2019,
  title = {Human {{Replay Spontaneously Reorganizes Experience}}},
  author = {Liu, Yunzhe and Dolan, Raymond J. and {Kurth-Nelson}, Zeb and Behrens, Timothy E.J.},
  year = {2019},
  month = jul,
  journal = {Cell},
  volume = {178},
  number = {3},
  pages = {640-652.e14},
  issn = {00928674},
  doi = {10.1016/j.cell.2019.06.012},
  abstract = {Knowledge abstracted from previous experiences can be transferred to aid new learning. Here, we asked whether such abstract knowledge immediately guides the replay of new experiences. We first trained participants on a rule defining an ordering of objects and then presented a novel set of objects in a scrambled order. Across two studies, we observed that representations of these novel objects were reactivated during a subsequent rest. As in rodents, human ``replay'' events occurred in sequences accelerated in time, compared to actual experience, and reversed their direction after a reward. Notably, replay did not simply recapitulate visual experience, but followed instead a sequence implied by learned abstract knowledge. Furthermore, each replay contained more than sensory representations of the relevant objects. A sensory code of object representations was preceded 50 ms by a code factorized into sequence position and sequence identity. We argue that this factorized representation facilitates the generalization of a previously learned structure to new objects.},
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}

@article{lizier_2011,
  title = {Multivariate Information-Theoretic Measures Reveal Directed Information Structure and Task Relevant Changes in {{fMRI}} Connectivity},
  author = {Lizier, Joseph T. and Heinzle, Jakob and Horstmann, Annette and Haynes, John-Dylan and Prokopenko, Mikhail},
  year = {2011},
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  journal = {Journal of Computational Neuroscience},
  volume = {30},
  number = {1},
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}

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  title = {A Simple Graphical Representation of Fourier-Based Imaging Methods},
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@article{lodewyckx_2011,
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@book{loeb_2016,
  title = {Real Analysis},
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  year = {2016},
  publisher = {{Springer Science+Business Media}},
  address = {{New York, NY}},
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@article{logothetis_2001,
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@article{logothetis_2004,
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@article{logothetis_2008,
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@article{lohmann_2010,
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@article{lohmann_2012,
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@article{london_,
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@article{lopesdasilva_2013,
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  number = {5},
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@article{lopez_,
  title = {Information {{Constraints}} on {{Auto-Encoding Variational Bayes}}},
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  abstract = {Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult to impose or assess structural constraints such as conditional independence. We propose a framework for learning representations that relies on auto-encoding variational Bayes, in which the search space is constrained via kernel-based measures of independence. In particular, our method employs the d-variable Hilbert-Schmidt Independence Criterion (dHSIC) to enforce independence between the latent representations and arbitrary nuisance factors. We show how this method can be applied to a range of problems, including problems that involve learning invariant and conditionally independent representations. We also present a full-fledged application to singlecell RNA sequencing (scRNA-seq). In this setting the biological signal is mixed in complex ways with sequencing errors and sampling effects. We show that our method outperforms the state-of-the-art approach in this domain.},
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}

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@article{lu_2006,
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}

@article{lu_2017,
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@article{lu_2017a,
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  month = nov,
  journal = {arXiv:1709.02540 [cs]},
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  abstract = {The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical results state that depth-bounded (e.g. depth-2) networks with suitable activation functions are universal approximators. We show a universal approximation theorem for width-bounded ReLU networks: width-(n + 4) ReLU networks, where n is the input dimension, are universal approximators. Moreover, except for a measure zero set, all functions cannot be approximated by width-n ReLU networks, which exhibits a phase transition. Several recent works demonstrate the benefits of depth by proving the depth-efficiency of neural networks. That is, there are classes of deep networks which cannot be realized by any shallow network whose size is no more than an exponential bound. Here we pose the dual question on the width-efficiency of ReLU networks: Are there wide networks that cannot be realized by narrow networks whose size is not substantially larger? We show that there exist classes of wide networks which cannot be realized by any narrow network whose depth is no more than a polynomial bound. On the other hand, we demonstrate by extensive experiments that narrow networks whose size exceed the polynomial bound by a constant factor can approximate wide and shallow network with high accuracy. Our results provide more comprehensive evidence that depth may be more effective than width for the expressiveness of ReLU networks.},
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@incollection{luce_1994,
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}

@book{luck_2014,
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  year = {2014},
  edition = {Second edition},
  publisher = {{The MIT Press}},
  address = {{Cambridge, Massachusetts}},
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@article{ludger_,
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@article{ludman_2010,
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  author = {Ludman, Evette J. and Fullerton, Stephanie M. and Spangler, Leslie and Trinidad, Susan Brown and Fujii, Monica M. and Jarvik, Gail P. and Larson, Eric B. and Burke, Wylie},
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@article{ludtke_2010,
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  issn = {1471-0056, 1471-0064},
  doi = {10.1038/nrg2360},
  abstract = {Recent advances in high-throughput genomic technologies are showing concrete results in the form of an increasing number of genome-wide association studies and in the publication of comprehensive individual genome\textendash phenome data sets. As a consequence of this flood of information the established concepts of research ethics are stretched to their limits, and issues of privacy, confidentiality and consent for research are being re-examined. Here, we show the feasibility of the co-development of scientific innovation and ethics, using the open-consent framework that was implemented in the Personal Genome Project as an example.},
  langid = {english},
  annotation = {00369},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2MSWETN5\\Lunshof et al. - 2008 - From genetic privacy to open consent.pdf;C\:\\Users\\dirko\\Zotero\\storage\\IIJ82U8Q\\Lunshof et al. - 2008 - From genetic privacy to open consent.pdf;C\:\\Users\\dirko\\Zotero\\storage\\JJNPTTKX\\Lunshof et al. - 2008 - From genetic privacy to open consent.pdf}
}

@book{lutz_2013,
  title = {Learning {{Python}}},
  author = {Lutz, Mark},
  year = {2013},
  edition = {Fifth edition},
  publisher = {{O'Reilly}},
  address = {{Beijing}},
  isbn = {978-1-4493-5573-9},
  langid = {english},
  lccn = {QA76.73.P98 L877 2013},
  keywords = {Object-oriented programming (Computer science),Python (Computer program language)},
  annotation = {00737},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3N7PREP2\\Lutz - 2013 - Learning Python.pdf}
}

@article{ma_2014,
  title = {Neural {{Coding}} of {{Uncertainty}} and {{Probability}}},
  author = {Ma, Wei Ji and Jazayeri, Mehrdad},
  year = {2014},
  journal = {Annual Review of Neuroscience},
  volume = {37},
  number = {1},
  pages = {205--220},
  issn = {0147-006X},
  doi = {10.1146/annurev-neuro-071013-014017},
  abstract = {Organisms must act in the face of sensory, motor, and reward uncertainty stemming from a pandemonium of stochasticity and missing information. In many tasks, organisms can make better decisions if they have at their disposal a representation of the uncertainty associated with task-relevant variables. We formalize this problem using Bayesian decision theory and review recent behavioral and neural evidence that the brain may use knowledge of uncertainty, confidence, and probability.},
  pmid = {25032495},
  keywords = {bayesian inference,decision making,perception,population encoding},
  annotation = {00000}
}

@article{ma_2019,
  title = {Bayesian {{Decision Models}}: {{A Primer}}},
  shorttitle = {Bayesian {{Decision Models}}},
  author = {Ma, Wei Ji},
  year = {2019},
  month = oct,
  journal = {Neuron},
  volume = {104},
  number = {1},
  pages = {164--175},
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.09.037},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VU8Q47ZQ\\Ma - 2019 - Bayesian Decision Models A Primer.pdf}
}

@article{macintyre_1992,
  title = {A Practical Approach to Calculating Luminance Contrast on a {{CRT}}},
  author = {MacIntyre, Blair and Cowan, William B.},
  year = {1992},
  month = oct,
  journal = {ACM Transactions on Graphics},
  volume = {11},
  number = {4},
  pages = {336--347},
  issn = {07300301},
  doi = {10.1145/146443.146467},
  abstract = {Luminance contrast is the basis of text legibility, and maintaining luminance contrast is essential for any colour selection algorithm. In principle, it can be calculated precisely on a sufficiently well calibrated display surface, but calibration is very expensive. Consequently, most current systems deal with contrast using heuristics. However, the usual CRT set-up puts the display surface into a state that is relatively predictable. Luminance values can be estimated based on this state, and these luminance values used to calculate contrast using the Michelson definition. This paper proposes a method for determining the contrast of coloured areas displayed on a CRT. It uses a contrast metric that is in wide use in visual psychophysics, and shows that the metric can be approximated reasonably without display measurement, as long as it is possible to assume that the CRT has been adjusted according to usual CRT set-up standards.},
  annotation = {00022},
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}

@article{mackay_,
  title = {Information {{Theory}}, {{Inference}}, and {{Learning Algorithms}}},
  author = {MacKay, David J C},
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}

@article{mackay_a,
  title = {Maximum {{Likelihood}} and {{Covariant Algorithms}} for {{Independent Component Analysis}}},
  author = {MacKay, David J C},
  pages = {15},
  abstract = {Bell and Sejnowski (1995) have derived a blind signal processing algorithm for a non-linear feedforward network from an information maximization viewpoint. This paper first shows that the same algorithm can be viewed as a maximum likelihood algorithm for the optimization of a linear generative model.},
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}

@article{madeleine_,
  title = {Centre for {{Intellectual Property Law}} ({{CIER}}), {{Molengraaff Institute}} for {{Private Law}}, {{Utrecht University}}, {{The Netherlands}},},
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}

@article{madsen_,
  title = {{{METHODS FOR NON-LINEAR LEAST SQUARES PROBLEMS}}},
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}

@article{magnus_1989,
  title = {Matrix {{Differential Calculus}} with {{Applications}} in {{Statistics}} and {{Econometrics}}.},
  author = {Magnus, Jan R. and Neudecker, Heinz},
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@article{magri_2009,
  title = {A Toolbox for the Fast Information Analysis of Multiple-Site {{LFP}}, {{EEG}} and Spike Train Recordings},
  author = {Magri, Cesare and Whittingstall, Kevin and Singh, Vanessa and Logothetis, Nikos K and Panzeri, Stefano},
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}

@article{magri_2012,
  title = {The {{Amplitude}} and {{Timing}} of the {{BOLD Signal Reflects}} the {{Relationship}} between {{Local Field Potential Power}} at {{Different Frequencies}}},
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  issn = {0270-6474, 1529-2401},
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@article{mahajan_2017,
  title = {Effect of {{EEG Referencing Methods}} on {{Auditory Mismatch Negativity}}},
  author = {Mahajan, Yatin and Peter, Varghese and Sharma, Mridula},
  year = {2017},
  month = oct,
  journal = {Frontiers in Neuroscience},
  volume = {11},
  pages = {560},
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  doi = {10.3389/fnins.2017.00560},
  abstract = {Auditory event-related potentials (ERPs) have consistently been used in the investigation of auditory and cognitive processing in the research and clinical laboratories. There is currently no consensus on the choice of appropriate reference for auditory ERPs. The most commonly used references in auditory ERP research are the mathematically linked-mastoids (LM) and average referencing (AVG). Since LM and AVG referencing procedures do not solve the issue of electrically-neutral reference, Reference Electrode Standardization Technique (REST) was developed to create a neutral reference for EEG recordings. The aim of the current research is to compare the influence of the reference on amplitude and latency of auditory mismatch negativity (MMN) as a function of magnitude of frequency deviance across three commonly used electrode montages (16, 32, and 64-channel) using REST, LM, and AVG reference procedures. The current study was designed to determine if the three reference methods capture the variation in amplitude and latency of MMN with the deviance magnitude. We recorded MMN from 12 normal hearing young adults in an auditory oddball paradigm with 1,000 Hz pure tone as standard and 1,030, 1,100, and 1,200 Hz as small, medium and large frequency deviants, respectively. The EEG data recorded to these sounds was re-referenced using REST, LM, and AVG methods across 16-, 32-, and 64-channel EEG electrode montages. Results revealed that while the latency of MMN decreased with increment in frequency of deviant sounds, no effect of frequency deviance was present for amplitude of MMN. There was no effect of referencing procedure on the experimental effect tested. The amplitude of MMN was largest when the ERP was computed using LM referencing and the REST referencing produced the largest amplitude of MMN for 64-channel montage. There was no effect of electrode-montage on AVG referencing induced ERPs. Contrary to our predictions, the results suggest that the auditory MMN elicited as a function of increments in frequency deviance does not depend on the choice of referencing procedure. The results also suggest that auditory ERPs generated using REST referencing is contingent on the electrode arrays more than the AVG referencing.},
  langid = {english},
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}

@article{makeig_1999,
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@article{makeig_2002,
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  author = {Makeig, S.},
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@article{makeig_2004,
  title = {Electroencephalographic {{Brain Dynamics Following Manually Responded Visual Targets}}},
  author = {Makeig, Scott and Delorme, Arnaud and Westerfield, Marissa and Jung, Tzyy-Ping and Townsend, Jeanne and Courchesne, Eric and Sejnowski, Terrence J},
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}

@article{makeig_2004a,
  title = {Mining Event-Related Brain Dynamics},
  author = {Makeig, Scott and Debener, Stefan and Onton, Julie and Delorme, Arnaud},
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  month = may,
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  doi = {10.1016/j.tics.2004.03.008},
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}

@inbook{mallot_2013,
  title = {Excitable {{Membranes}} and {{Neural Conduction}}},
  booktitle = {Computational {{Neuroscience}}},
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  pages = {1--21},
  publisher = {{Springer International Publishing}},
  address = {{Heidelberg}},
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}

@article{mandelkow_2006,
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  doi = {10.1016/j.neuroimage.2006.04.231},
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@article{mangun_1998,
  title = {{{ERP}} and {{fMRI}} Measures of Visual Spatial Selective Attention},
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}

@book{manly_2017,
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  author = {Manly, Bryan F. J. and Navarro Alberto, Jorge A.},
  year = {2017},
  edition = {Fourth edition},
  publisher = {{CRC Press, Taylor \& Francis Group}},
  address = {{Boca Raton}},
  isbn = {978-1-4987-2896-6},
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@article{mann_1943,
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@article{manski_2019,
  title = {Treatment {{Choice With Trial Data}}: {{Statistical Decision Theory Should Supplant Hypothesis Testing}}},
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  author = {Manski, Charles F.},
  year = {2019},
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  journal = {The American Statistician},
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  number = {sup1},
  pages = {296--304},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2018.1513377},
  abstract = {A central objective of empirical research on treatment response is to inform treatment choice. Unfortunately, researchers commonly use concepts of statistical inference whose foundations are distant from the problem of treatment choice. It has been particularly common to use hypothesis tests to compare treatments. Wald's development of statistical decision theory provides a coherent frequentist framework for use of sample data on treatment response to make treatment decisions. A body of recent research applies statistical decision theory to characterize uniformly satisfactory treatment choices, in the sense of maximum loss relative to optimal decisions (also known as maximum regret). This article describes the basic ideas and findings, which provide an appealing practical alternative to use of hypothesis tests. For simplicity, the article focuses on medical treatment with evidence from classical randomized clinical trials. The ideas apply generally, encompassing use of observational data and treatment choice in nonmedical contexts.},
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@article{maris_2007,
  title = {Nonparametric Statistical Testing of {{EEG-}} and {{MEG-data}}},
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@article{markovic_2016,
  title = {Comparative {{Analysis}} of {{Behavioral Models}} for {{Adaptive Learning}} in {{Changing Environments}}},
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@article{marnau_2016,
  title = {{Anonymisierung, Pseudonymisierung und Transparenz f\"ur Big Data: Technische Herausforderungen und Regelungen in der Datenschutz-Grundverordnung}},
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  month = jul,
  journal = {Datenschutz und Datensicherheit - DuD},
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  number = {7},
  pages = {428--433},
  issn = {1614-0702, 1862-2607},
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@inproceedings{marrelec_2002,
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  publisher = {{AIP}},
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  abstract = {Functional MRI (fMRI) is a recent, non-invasive technique allowing for the evolution of brain processes to be dynamically followed in various cognitive or behavioral tasks. In BOLD fMRI, what is actually measured is only indirectly related to neuronal activity through a process that is still under investigation. A convenient way to analyze BOLD fMRI data consists of considering the whole brain as a system characterized by a transfer response function, called the Hemodynamic Response Function (HRF). Precise and robust estimation of the HRF has not been achieved yet: parametric methods tend to be robust but require too strong constraints on the shape of the HRF, whereas non-parametric models are not reliable since the problem is badly conditioned.},
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@article{mars_2008,
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@article{mars_2012,
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@article{maxmen_,
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@article{mayhew_2016,
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@article{mazzoni_2008,
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@article{mcfarquhar_,
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  abstract = {Group-level repeated measurements are common in neuroimaging, yet their analysis remains complex. Although a variety of specialised tools now exist, it is surprising that to-date there has been no clear discussion of how repeatedmeasurements can be appropriately modelled using the standard general linear model approach, implemented in software such as SPM and FSL. To understand how this can be achieved, two important concepts must be discussed. The first is the use of expected mean squares in defining suitable F-ratios for testing hypotheses. The second is the use of estimable functions in defining suitable contrast weights in overparameterised designs. In this paper, both of these concepts will be discussed in detail, paying particular attention to how they apply in complex repeated-measurement analyses. A set of simple steps will then be given alongside an example of specifying a 3-way repeated-measures ANOVA in SPM. Equivalency of the results compared to other standard statistical software will also be demonstrated. The hope is that this paper will provide some clarity on this confusing topic, giving researchers the confidence to correctly specify these forms of models in standard software packages.},
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@article{mcfarquhar_2016,
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@article{mclachlan_,
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@article{mclaren_2012,
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@article{mclennan_2020,
  title = {An Embedded Ethics Approach for {{AI}} Development},
  author = {McLennan, Stuart and Fiske, Amelia and Celi, Leo Anthony and M{\"u}ller, Ruth and Harder, Jan and Ritt, Konstantin and Haddadin, Sami and Buyx, Alena},
  year = {2020},
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  journal = {Nature Machine Intelligence},
  volume = {2},
  number = {9},
  pages = {488--490},
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  doi = {10.1038/s42256-020-0214-1},
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@article{mcmahan_,
  title = {Bounded {{Real-Time Dynamic Programming}}: {{RTDP}} with Monotone Upper Bounds and Performance Guarantees},
  author = {McMahan, H Brendan and Likhachev, Maxim and Gordon, Geoffrey J},
  pages = {8},
  abstract = {MDPs are an attractive formalization for planning, but realistic problems often have intractably large state spaces. When we only need a partial policy to get from a fixed start state to a goal, restricting computation to states relevant to this task can make much larger problems tractable. We introduce a new algorithm, Bounded RTDP, which can produce partial policies with strong performance guarantees while only touching a fraction of the state space, even on problems where other algorithms would have to visit the full state space. To do so, Bounded RTDP maintains both upper and lower bounds on the optimal value function. The performance of Bounded RTDP is greatly aided by the introduction of a new technique to efficiently find suitable upper bounds; this technique can also be used to provide informed initialization to a wide range of other planning algorithms.},
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@article{mcnutt_2018,
  title = {Transparency in Authors' Contributions and Responsibilities to Promote Integrity in Scientific Publication},
  author = {McNutt, Marcia K. and Bradford, Monica and Drazen, Jeffrey M. and Hanson, Brooks and Howard, Bob and Jamieson, Kathleen Hall and Kiermer, V{\'e}ronique and Marcus, Emilie and Pope, Barbara Kline and Schekman, Randy and Swaminathan, Sowmya and Stang, Peter J. and Verma, Inder M.},
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  journal = {Proceedings of the National Academy of Sciences},
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  number = {11},
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  doi = {10.1073/pnas.1715374115},
  abstract = {In keeping with the growing movement in scientific publishing toward transparency in data and methods, we propose changes to journal authorship policies and procedures to provide insight into which author is responsible for which contributions, better assurance that the list is complete, and clearly articulated standards to justify earning authorship credit. To accomplish these goals, we recommend that journals adopt common and transparent standards for authorship, outline responsibilities for corresponding authors, adopt the Contributor Roles Taxonomy (CRediT) (               docs.casrai.org/CRediT               ) methodology for attributing contributions, include this information in article metadata, and require authors to use the ORCID persistent digital identifier (               https://orcid.org               ). Additionally, we recommend that universities and research institutions articulate expectations about author roles and responsibilities to provide a point of common understanding for discussion of authorship across research teams. Furthermore, we propose that funding agencies adopt the ORCID identifier and accept the CRediT taxonomy. We encourage scientific societies to further authorship transparency by signing on to these recommendations and promoting them through their meetings and publications programs.},
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  title = {A Dynamic Causal Modeling Study on Category Effects: {{Bottom}}\textendash up or Top\textendash down Mediation?},
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@article{mechelli_2004,
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@book{meintrup_2005,
  title = {{Stochastik: Theorie und Anwendungen}},
  shorttitle = {{Stochastik}},
  author = {Meintrup, David and Sch{\"a}ffler, Stefan},
  year = {2005},
  series = {{Statistik und ihre Anwendungen}},
  publisher = {{Springer}},
  address = {{Berlin Heidelberg New York}},
  abstract = {Stochastische Methoden besitzenin vielenTeilen der Technik und Informatik eine hohe Relevanz. So beruhen z.B. die meisten modernen Verfahren der digitalen Nachrichten\"ubertragung, Schaltkreissimulation aber auch Verfahrenstechnik und des Financial Engineeringaufstochastisches Prinzipien. Das Buch bietet eine fundierte und anwendungsbezogene Einf\"uhrung in die Wahrscheinlichkeitstheorie und Statistik. Im Zentrum stehen nach einer grundlegenden Behandlung der Ma\ss - und Integrationstheorie sowie der Grundlagen der Wahrscheinlichkeitstheorie stochastische Prozesse, insbesonders Poisson-Prozesse, Martingale und Brownsche Bewegung. Alle Resultate werden ausf\"uhrlich motiviert und exakt bewiesen. Dadurch eignet sich das Buch hervorragend zum Selbststudium und als vorlesungsbegleitende Literatur. TOC:Ma\ss theorie.- Wahrscheinlichkeitstheorie.- Stochastische Prozesse.- Mathematische Statistik und statistische Datenanalyse.- Anhang},
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  author = {Meiss, J. D.},
  year = {2007},
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  publisher = {{Society for Industrial and Applied Mathematics}},
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@article{mell_2009,
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@article{menon_2010,
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@article{mensh_2017,
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@article{meyniel_2015,
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@article{miezin_2000,
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@article{mihatsch_2002,
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@article{milham_2018,
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@article{millman_2018,
  title = {Teaching {{Computational Reproducibility}} for {{Neuroimaging}}},
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  abstract = {We describe a project-based introduction to reproducible and collaborative neuroimaging analysis. Traditional teaching on neuroimaging usually consists of a series of lectures that emphasize the big picture rather than the foundations on which the techniques are based. The lectures are often paired with practical workshops in which students run imaging analyses using the graphical interface of specific neuroimaging software packages. Our experience suggests that this combination leaves the student with a superficial understanding of the underlying ideas, and an informal, inefficient, and inaccurate approach to analysis. To address these problems, we based our course around a substantial open-ended group project. This allowed us to teach: (a) computational tools to ensure computationally reproducible work, such as the Unix command line, structured code, version control, automated testing, and code review and (b) a clear understanding of the statistical techniques used for a basic analysis of a single run in an MR scanner. The emphasis we put on the group project showed the importance of standard computational tools for accuracy, efficiency, and collaboration. The projects were broadly successful in engaging students in working reproducibly on real scientific questions. We propose that a course on this model should be the foundation for future programs in neuroimaging. We believe it will also serve as a model for teaching efficient and reproducible research in other fields of computational science.},
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@article{Millman2018,
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}

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}

@article{minati_2012,
  title = {Neural {{Signatures}} of {{Economic Parameters During Decision-Making}}: {{A Functional MRI}} ({{fMRI}}), {{Electroencephalography}} ({{EEG}}) and {{Autonomic Monitoring Study}}},
  shorttitle = {Neural {{Signatures}} of {{Economic Parameters During Decision-Making}}},
  author = {Minati, Ludovico and Grisoli, Marina and Franceschetti, Silvana and Epifani, Francesca and Granvillano, Alice and Medford, Nick and Harrison, Neil A. and Piacentini, Sylvie and Critchley, Hugo D.},
  year = {2012},
  month = jan,
  journal = {Brain Topography},
  volume = {25},
  number = {1},
  pages = {73--96},
  issn = {0896-0267, 1573-6792},
  doi = {10.1007/s10548-011-0210-1},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3DWYSEDL\\Minati et al_2012_Brain Topography_Neural Signatures of Economic Parameters During Decision-Making.pdf}
}

@book{minsky_1969,
  title = {Perceptrons: An Introduction to Computational Geometry},
  shorttitle = {Perceptrons},
  author = {Minsky, Marvin and Papert, Seymour A.},
  year = {1969},
  edition = {2. print. with corr},
  publisher = {{The MIT Press}},
  address = {{Cambridge/Mass.}},
  isbn = {978-0-262-13043-1 978-0-262-63022-1},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\QZX43SV9\\Marvin Minsky, Seymour A. Papert - Perceptrons_ An Introduction to Computational Geometry-The MIT Press (1987).pdf}
}

@article{mirzajani_2007,
  title = {Spatial Frequency Modulates Visual Cortical Response to Temporal Frequency Variation of Visual Stimuli: An {{fMRI}} Study},
  shorttitle = {Spatial Frequency Modulates Visual Cortical Response to Temporal Frequency Variation of Visual Stimuli},
  author = {Mirzajani, A and {Riyahi-Alam}, Nader and Oghabian, Mohammad Ali and Saberi, H and Firouznia, K},
  year = {2007},
  month = may,
  journal = {Physiological Measurement},
  volume = {28},
  number = {5},
  pages = {547--554},
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}

@article{mishachev_2017,
  title = {Backpropagation in Matrix Notation},
  author = {Mishachev, N. M.},
  year = {2017},
  journal = {arXiv:1707.02746},
  eprint = {1707.02746},
  eprinttype = {arxiv},
  abstract = {In this note we calculate the gradient of the network function in matrix notation.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Neural and Evolutionary Computing},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SBCBX4CE\\Mishachev - 2017 - Backpropagation in matrix notation.pdf}
}

@article{misra_2005,
  title = {Estimation of the Entropy of a Multivariate Normal Distribution},
  author = {Misra, Neeraj and Singh, Harshinder and Demchuk, Eugene},
  year = {2005},
  month = feb,
  journal = {Journal of Multivariate Analysis},
  volume = {92},
  number = {2},
  pages = {324--342},
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  doi = {10.1016/j.jmva.2003.10.003},
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}

@article{mitra_1997,
  title = {The Nature of Spatiotemporal Changes in Cerebral Hemodynamics as Manifested in Functional Magnetic Resonance Imaging},
  author = {Mitra, Partha P. and Ogawa, Seiji and Hu, Xiaoping and U??urbil, K??mil},
  year = {1997},
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  doi = {10.1002/mrm.1910370407},
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}

@article{mkrtchian_2017,
  title = {Modeling {{Avoidance}} in {{Mood}} and {{Anxiety Disorders Using Reinforcement Learning}}},
  author = {Mkrtchian, Anahit and Aylward, Jessica and Dayan, Peter and Roiser, Jonathan P. and Robinson, Oliver J.},
  year = {2017},
  month = oct,
  journal = {Biological Psychiatry},
  volume = {82},
  number = {7},
  pages = {532--539},
  issn = {00063223},
  doi = {10.1016/j.biopsych.2017.01.017},
  abstract = {BACKGROUND: Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5\% of all adult years lived with disability in the developed world. Avoidance behavior\textemdash avoiding social situations for fear of embarrassment, for instance\textemdash is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. METHODS: Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. RESULTS: We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. CONCLUSIONS: This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders.},
  langid = {english},
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}

@article{mnih_2015,
  title = {Human-Level Control through Deep Reinforcement Learning},
  author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K. and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
  year = {2015},
  month = feb,
  journal = {Nature},
  volume = {518},
  number = {7540},
  pages = {529--533},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature14236},
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  annotation = {11484},
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}

@book{moeschlin_2000,
  title = {Angewandte {{Statistik}}},
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}

@book{moeschlin_2000a,
  title = {Sch\"atztheorie},
  author = {Moeschlin, O},
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}

@book{moeschlin_2000b,
  title = {Wahrscheinlichkeitstheorie {{I}}},
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}

@book{moeschlin_2000c,
  title = {{Wahrscheinlichkeitstheorie II}},
  author = {Moeschlin, O},
  year = {2000},
  langid = {german},
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}

@incollection{mohammad-djafari_1998,
  title = {Probabilistic {{Methods}} for {{Data Fusion}}},
  booktitle = {Maximum {{Entropy}} and {{Bayesian Methods}}},
  author = {{Mohammad-Djafari}, Ali},
  editor = {Erickson, Gary J. and Rychert, Joshua T. and Smith, C. Ray},
  year = {1998},
  pages = {57--69},
  publisher = {{Springer Netherlands}},
  address = {{Dordrecht}},
  doi = {10.1007/978-94-011-5028-6_5},
  abstract = {The main object of this paper is to show how we can use classical probabilistic methods such as Maximum Entropy (ME), maximum likelihood (ML) and/or Bayesian (BAYES) approaches to do microscopic and macroscopic data fusion. Actually ME can be used to assign a probability law to an unknown quantity when we have macroscopic data (expectations) on it. ML can be used to estimate the parameters of a probability law when we have microscopic data (direct observation). BAYES can be used to update a prior probability law when we have microscopic data through the likelihood. When we have both microscopic and macroscopic data we can use first ME to assign a prior and then use BAYES to update it to the posterior law thus doing the desired data fusion. However, in practical data fusion applications, we may still need some engineering feeling to propose realistic data fusion solutions. Some simple examples in sensor data fusion and image reconstruction using different kind of data are presented to illustrate these ideas.},
  isbn = {978-94-010-6111-7 978-94-011-5028-6},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AIUVH2TG\\Mohammad-Djafari - 1998 - Probabilistic Methods for Data Fusion.pdf}
}

@article{mohr_2010,
  title = {Neural {{Processing}} of {{Risk}}},
  author = {Mohr, P. N. C. and Biele, G. and Heekeren, H. R.},
  year = {2010},
  month = may,
  journal = {Journal of Neuroscience},
  volume = {30},
  number = {19},
  pages = {6613--6619},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.0003-10.2010},
  langid = {english},
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}

@article{molholm_2004,
  title = {Multisensory {{Visual-Auditory Object Recognition}} in {{Humans}}: A {{High-density Electrical Mapping Study}}},
  shorttitle = {Multisensory {{Visual-Auditory Object Recognition}} in {{Humans}}},
  author = {Molholm, S.},
  year = {2004},
  month = apr,
  journal = {Cerebral Cortex},
  volume = {14},
  number = {4},
  pages = {452--465},
  issn = {1460-2199},
  doi = {10.1093/cercor/bhh007},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\T5TKBBBF\\Molholm_2004_Cerebral Cortex_Multisensory Visual-Auditory Object Recognition in Humans.pdf}
}

@article{momennejad_2017,
  title = {The Successor Representation in Human Reinforcement Learning},
  author = {Momennejad, I. and Russek, E. M. and Cheong, J. H. and Botvinick, M. M. and Daw, N. D. and Gershman, S. J.},
  year = {2017},
  journal = {Nature Human Behaviour},
  volume = {1},
  number = {9},
  pages = {680--692},
  issn = {23973374},
  doi = {10.1038/s41562-017-0180-8},
  abstract = {Theories of reinforcement learning in neuroscience have focused on two families of algorithms. Model-free algorithms cache action values, making them cheap but inflexible: a candidate mechanism for adaptive and maladaptive habits. Model-based algorithms achieve flexibility at computational expense, by rebuilding values from a model of the environment. We examine an intermediate class of algorithms, the successor representation (SR), which caches long-run state expectancies, blending model-free efficiency with model-based flexibility. Although previous reward revaluation studies distinguish model-free from model-based learning algorithms, such designs cannot discriminate between model-based and SR-based algorithms, both of which predict sensitivity to reward revaluation. However, changing the transition structure (" transition revaluation ") should selectively impair revaluation for the SR. In two studies we provide evidence that humans are differentially sensitive to reward vs. transition revaluation, consistent with SR predictions. These results support a new neuro-computational mechanism for flexible choice, while introducing a subtler, more cognitive notion of habit.},
  annotation = {00000}
}

@article{monahan_1982,
  title = {Optimal Stopping in a Partially Observable Binary-Valued Markov Chain with Costly Perfect Information},
  author = {Monahan, George E.},
  year = {1982},
  month = mar,
  journal = {Journal of Applied Probability},
  volume = {19},
  number = {01},
  pages = {72--81},
  issn = {0021-9002, 1475-6072},
  doi = {10.2307/3213917},
  langid = {english},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\H8NS2BKI\\Monahan_1982_Journal of Applied Probability_Optimal stopping in a partially observable binary-valued markov chain with.pdf}
}

@article{mongillo_2008,
  title = {Online Learning with Hidden {{Markov}} Models},
  author = {Mongillo, Gianluigi and Deneve, Sophie},
  year = {2008},
  journal = {Neural computation},
  volume = {20},
  number = {7},
  pages = {1706--1716},
  annotation = {00093},
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}

@article{montemurro_2007,
  title = {Tight Data-Robust Bounds to Mutual Information Combining Shuffling and Model Selection Techniques},
  author = {Montemurro, Marcelo A. and Senatore, Riccardo and Panzeri, Stefano},
  year = {2007},
  journal = {Neural Computation},
  volume = {19},
  number = {11},
  pages = {2913--2957},
  annotation = {00075},
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}

@article{monti_2011,
  title = {Statistical {{Analysis}} of {{fMRI Time-Series}}: {{A Critical Review}} of the {{GLM Approach}}},
  shorttitle = {Statistical {{Analysis}} of {{fMRI Time-Series}}},
  author = {Monti, Martin},
  year = {2011},
  journal = {Frontiers in Human Neuroscience},
  volume = {5},
  issn = {16625161},
  doi = {10.3389/fnhum.2011.00028},
  annotation = {00000},
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}

@article{montufar_2014,
  title = {On the {{Number}} of {{Linear Regions}} of {{Deep Neural Networks}}},
  author = {Mont{\'u}far, Guido and Pascanu, Razvan and Cho, Kyunghyun and Bengio, Yoshua},
  year = {2014},
  month = feb,
  journal = {arXiv:1402.1869 [cs, stat]},
  eprint = {1402.1869},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map portions of each layer's input-space to the same output. In this way, deep models compute functions that react equally to complicated patterns of different inputs. The compositional structure of these functions enables them to re-use pieces of computation exponentially often in terms of the network's depth. This paper investigates the complexity of such compositional maps and contributes new theoretical results regarding the advantage of depth for neural networks with piecewise linear activation functions. In particular, our analysis is not specific to a single family of models, and as an example, we employ it for rectifier and maxout networks. We improve complexity bounds from pre-existing work and investigate the behavior of units in higher layers.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing,Statistics - Machine Learning},
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}

@book{moosbrugger_2012,
  title = {{Testtheorie und Fragebogenkonstruktion: mit 66 Abbildungen und 41 Tabellen}},
  shorttitle = {{Testtheorie und Fragebogenkonstruktion}},
  editor = {Moosbrugger, Helfried and Kelava, Augustin},
  year = {2012},
  series = {{Springer-Lehrbuch}},
  edition = {2., aktualisierte und \"uberarbeitete Auflage},
  publisher = {{Springer}},
  address = {{Berlin Heidelberg}},
  isbn = {978-3-642-20071-7 978-3-642-20072-4},
  langid = {german},
  annotation = {00000  OCLC: 844892841},
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}

@article{moosmann_2008,
  title = {Joint Independent Component Analysis for Simultaneous {{EEG}}\textendash{{fMRI}}: {{Principle}} and Simulation},
  shorttitle = {Joint Independent Component Analysis for Simultaneous {{EEG}}\textendash{{fMRI}}},
  author = {Moosmann, Matthias and Eichele, Tom and Nordby, Helge and Hugdahl, Kenneth and Calhoun, Vince D.},
  year = {2008},
  month = mar,
  journal = {International Journal of Psychophysiology},
  volume = {67},
  number = {3},
  pages = {212--221},
  issn = {01678760},
  doi = {10.1016/j.ijpsycho.2007.05.016},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZL74Q7JV\\Moosmann et al_2008_International Journal of Psychophysiology_Joint independent component analysis for simultaneous EEG–fMRI.pdf}
}

@article{moran_2007,
  title = {A Neural Mass Model of Spectral Responses in Electrophysiology},
  author = {Moran, R.J. and Kiebel, S.J. and Stephan, K.E. and Reilly, R.B. and Daunizeau, J. and Friston, K.J.},
  year = {2007},
  month = sep,
  journal = {NeuroImage},
  volume = {37},
  number = {3},
  pages = {706--720},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.05.032},
  langid = {english},
  annotation = {00000},
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}

@article{moran_2008,
  title = {Bayesian Estimation of Synaptic Physiology from the Spectral Responses of Neural Masses},
  author = {Moran, R.J. and Stephan, K.E. and Kiebel, S.J. and Rombach, N. and O'Connor, W.T. and Murphy, K.J. and Reilly, R.B. and Friston, K.J.},
  year = {2008},
  month = aug,
  journal = {NeuroImage},
  volume = {42},
  number = {1},
  pages = {272--284},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.01.025},
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  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AV8TKZ7J\\Moran et al_2008_NeuroImage_Bayesian estimation of synaptic physiology from the spectral responses of.pdf}
}

@article{moran_2009,
  title = {Dynamic Causal Models of Steady-State Responses},
  author = {Moran, R.J. and Stephan, K.E. and Seidenbecher, T. and Pape, H.-C. and Dolan, R.J. and Friston, K.J.},
  year = {2009},
  month = feb,
  journal = {NeuroImage},
  volume = {44},
  number = {3},
  pages = {796--811},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.09.048},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\L3CZWVE3\\Moran et al_2009_NeuroImage_Dynamic causal models of steady-state responses.pdf}
}

@article{moran_2011,
  title = {An {{In Vivo Assay}} of {{Synaptic Function Mediating Human Cognition}}},
  author = {Moran, Rosalyn~J. and Symmonds, Mkael and Stephan, Klaas~E. and Friston, Karl~J. and Dolan, Raymond~J.},
  year = {2011},
  month = aug,
  journal = {Current Biology},
  volume = {21},
  number = {15},
  pages = {1320--1325},
  issn = {09609822},
  doi = {10.1016/j.cub.2011.06.053},
  abstract = {The contribution of dopamine to working memory has been studied extensively [1\textendash 3]. Here, we exploited its well characterized effects [1\textendash 3] to validate a novel human in vivo assay of ongoing synaptic [4, 5] processing. We obtained magnetoencephalographic (MEG) measurements from subjects performing a working memory (WM) task during a withinsubject, placebo-controlled, pharmacological (dopaminergic) challenge. By applying dynamic causal modeling (DCM), a Bayesian technique for neuronal system identification [6], to MEG signals from prefrontal cortex, we demonstrate that it is possible to infer synaptic signaling by specific ion channels in behaving humans. Dopamineinduced enhancement of WM performance was accompanied by significant changes in MEG signal power, and a DCM assay disclosed related changes in synaptic signaling. By estimating the contribution of ionotropic receptors (AMPA, NMDA, and GABAA) to the observed spectral response, we demonstrate changes in their function commensurate with the synaptic effects of dopamine. The validity of our model is reinforced by a striking quantitative effect on NMDA and AMPA receptor signaling that predicted behavioral improvement over subjects. Our results provide a proof-of-principle demonstration of a novel framework for inferring, noninvasively, neuromodulatory influences on ion channel signaling via specific ionotropic receptors, providing a window on the hidden synaptic events mediating discrete psychological processes in humans.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\S5Z6YAHI\\Moran et al. - 2011 - An In Vivo Assay of Synaptic Function Mediating Hu.pdf}
}

@article{moran_2011a,
  title = {Dynamic {{Causal Models}} and {{Physiological Inference}}: {{A Validation Study Using Isoflurane Anaesthesia}} in {{Rodents}}},
  shorttitle = {Dynamic {{Causal Models}} and {{Physiological Inference}}},
  author = {Moran, Rosalyn J. and Jung, Fabienne and Kumagai, Tetsuya and Endepols, Heike and Graf, Rudolf and Dolan, Raymond J. and Friston, Karl J. and Stephan, Klaas E. and Tittgemeyer, Marc},
  editor = {Uversky, Vladimir N.},
  year = {2011},
  month = aug,
  journal = {PLoS ONE},
  volume = {6},
  number = {8},
  pages = {e22790},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0022790},
  langid = {english},
  annotation = {00000},
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}

@article{moran_2013,
  title = {Neural Masses and Fields in Dynamic Causal Modeling},
  author = {Moran, Rosalyn and Pinotsis, Dimitris A. and Friston, Karl},
  year = {2013},
  journal = {Frontiers in Computational Neuroscience},
  volume = {7},
  issn = {1662-5188},
  doi = {10.3389/fncom.2013.00057},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UFWLP3M6\\Moran et al_2013_Frontiers in Computational Neuroscience_Neural masses and fields in dynamic causal modeling.pdf}
}

@incollection{more_1978,
  title = {The {{Levenberg-Marquardt}} Algorithm: {{Implementation}} and Theory},
  shorttitle = {The {{Levenberg-Marquardt}} Algorithm},
  booktitle = {Numerical {{Analysis}}},
  author = {Mor{\'e}, Jorge J.},
  editor = {Watson, G. A.},
  year = {1978},
  volume = {630},
  pages = {105--116},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/BFb0067700},
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}

@article{morris_1983,
  title = {Parametric {{Empirical Bayes Inference}}: {{Theory}} and {{Applications}}},
  shorttitle = {Parametric {{Empirical Bayes Inference}}},
  author = {Morris, Carl N.},
  year = {1983},
  month = mar,
  journal = {Journal of the American Statistical Association},
  volume = {78},
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}

@article{morrison_2017,
  title = {The {{European General Data Protection Regulation}}: Challenges and Considerations for {{iPSC}} Researchers and Biobanks},
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  author = {Mosher, J.C. and Leahy, R.M. and Lewis, P.S.},
  year = {1999},
  month = mar,
  journal = {IEEE Transactions on Biomedical Engineering},
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  abstract = {A solution of the forward problem is an important component of any method for computing the spatio-temporal activity of the neural sources of magnetoencephalography (MEG) and electroencephalography (EEG) data. The forward problem involves computing the scalp potentials or external magnetic field at a finite set of sensor locations for a putative source configuration. We present a unified treatment of analytical and numerical solutions of the forward problem in a form suitable for use in inverse methods. This formulation is achieved through factorization of the lead field into the product of the moment of the elemental current dipole source with a ``kernel matrix'' that depends on the head geometry and source and sensor locations, and a ``sensor matrix'' that models sensor orientation and gradiometer effects in MEG and differential measurements in EEG. Using this formulation and a recently developed approximation formula for EEG, based on the ``Berg parameters,'' we present novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG. We also present novel investigations of different boundary element methods (BEM's) and present evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods. Explicit expressions for the matrix kernels for MEG and EEG for spherical and realistic head geometries are included.},
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  abstract = {Commentary: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates},
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  keywords = {False positive error,false positive results,family-wise error rate,fMRI,functional magnetic resonance imaging,Gaussian random field,random field theory},
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@incollection{mumford_2015,
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  doi = {10.1016/B978-0-12-397025-1.00318-3},
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  year = {2015},
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  number = {4},
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  publisher = {{National Academies Press}},
  address = {{Washington, DC}},
  abstract = {"One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science"--},
  isbn = {978-0-309-48616-3},
  langid = {english},
  lccn = {Q180.A1 N27 2019},
  keywords = {Experiments,Methodology,Reproducible research,Science,United States},
  annotation = {OCLC: on1102598241},
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}

@article{naudts_,
  title = {Best {{Practices}}, {{Blueprints}} and {{Policy Guidelines}} for {{Open Access}} to {{Scientific Information}}},
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  pages = {69},
  abstract = {Open access data publishing is the act of making data freely available online so that they can be downloaded, analysed, re-used and cited by other people than the creators of the data. This can be achieved in various ways. In the broadest sense, any upload of a dataset onto a freely accessible website could be regarded as ``data publishing''.},
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@article{neftci_2019,
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  series = {The {{Student Mathematical Library}}},
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@article{nichols_2005,
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@article{nichols_2017,
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@article{nishimura_2013,
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  abstract = {Background: Obtaining informed consent is a cornerstone of biomedical research, yet participants comprehension of presented information is often low. The most effective interventions to improve understanding rates have not been identified. Purpose: To systematically analyze the random controlled trials testing interventions to research informed consent process. The primary outcome of interest was quantitative rates of participant understanding; secondary outcomes were rates of information retention, satisfaction, and accrual. Interventional categories included multimedia, enhanced consent documents, extended discussions, test/feedback quizzes, and miscellaneous methods. Methods: The search spanned from database inception through September 2010. It was run on Ovid MEDLINE, Ovid EMBASE, Ovid CINAHL, Ovid PsycInfo and Cochrane CENTRAL, ISI Web of Science and Scopus. Five reviewers working independently and in duplicate screened full abstract text to determine eligibility. We included only RCTs. 39 out of 1523 articles fulfilled review criteria (2.6\%), with a total of 54 interventions. A data extraction form was created in Distiller, an online reference management system, through an iterative process. One author collected data on study design, population, demographics, intervention, and analytical technique. Results: Meta-analysis was possible on 22 interventions: multimedia, enhanced form, and extended discussion categories; all 54 interventions were assessed by review. Meta-analysis of multimedia approaches was associated with a non-significant increase in understanding scores (SMD 0.30, 95\% CI, -0.23 to 0.84); enhanced consent form, with significant increase (SMD 1.73, 95\% CI, 0.99 to 2.47); and extended discussion, with significant increase (SMD 0.53, 95\% CI, 0.21 to 0.84). By review, 31\% of multimedia interventions showed significant improvement in understanding; 41\% for enhanced consent form; 50\% for extended discussion; 33\% for test/feedback; and 29\% for miscellaneous. Multiple sources of variation existed between included studies: control processes, the presence of a human proctor, real vs. simulated protocol, and assessment formats. Conclusions: Enhanced consent forms and extended discussions were most effective in improving participant understanding. Interventions of all categories had no negative impact on participant satisfaction or study accrual. Identification of best practices for studies of informed consent interventions would aid future systematic comparisons.},
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@article{niv_2016,
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@article{nunez_2019,
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  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1603583113},
  abstract = {Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner.},
  copyright = {\textcopyright{}  . Freely available online through the PNAS open access option.},
  langid = {english},
  pmid = {27930289},
  keywords = {consciousness,information geometry,integrated information,mutual information,transfer entropy},
  annotation = {00025},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8UIFKH98\\Oizumi et al_2016_Proceedings of the National Academy of Sciences_Unified framework for information integration based on information geometry.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PMZ6YMHX\\Oizumi et al. - 2016 - Unified framework for information integration base.pdf;C\:\\Users\\dirko\\Zotero\\storage\\DP7RP4XY\\14817.html}
}

@article{oja_2004,
  title = {Multivariate {{Nonparametric Tests}}},
  author = {Oja, Hannu and Randles, Ronald H.},
  year = {2004},
  journal = {Statistical Science},
  volume = {19},
  number = {4},
  pages = {598--605},
  abstract = {Multivariatenonparametricstatisticaltests of hypothesesaredescribed for the one-sample location problem, the several-samplelocation problemand the problemof testing independencebetween pairsof vectors. Thesemethodsarebasedon affine-invarianstpatialsign andspatialrankvectors.Theyprovideaffine-invarianmt ultivariategeneralizationsof theunivariate sign test, signed-ranktest, Wilcoxon ranksum test, Kruskal-Wallistest, and the Kendalland Spearmancorrelationtests. While the emphasis is on tests of hypotheses,certainreferencesto associatedaffine-equivarianetstimatorsare included.Pitmanasymptoticefficiencies demonstratethe excellent performanceof these methods,particularlyin heavy-tailedpopulation settings. Moreover,these methodsare easy to computefor datain common dimensions.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YJIJA96P\\Oja und Randles - 2004 - Multivariate Nonparametric Tests.pdf}
}

@article{okeefe_2007,
  title = {Brief {{Report}}: {{Post Hoc Power}}, {{Observed Power}}, {{A Priori Power}}, {{Retrospective Power}}, {{Prospective Power}}, {{Achieved Power}}: {{Sorting Out Appropriate Uses}} of {{Statistical Power Analyses}}},
  shorttitle = {Brief {{Report}}},
  author = {O'Keefe, Daniel J.},
  year = {2007},
  month = dec,
  journal = {Communication Methods and Measures},
  volume = {1},
  number = {4},
  pages = {291--299},
  issn = {1931-2458, 1931-2466},
  doi = {10.1080/19312450701641375},
  langid = {english},
  annotation = {00150},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HDX23CEF\\O'Keefe - 2007 - Brief Report Post Hoc Power, Observed Power, A Pr.pdf}
}

@article{olgac_2002,
  title = {An Exact Method for the Stability Analysis of Time-Delayed Linear Time-Invariant ({{LTI}}) Systems},
  author = {Olgac, N. and Sipahi, R.},
  year = {2002},
  month = may,
  journal = {IEEE Transactions on Automatic Control},
  volume = {47},
  number = {5},
  pages = {793--797},
  issn = {0018-9286},
  doi = {10.1109/TAC.2002.1000275},
  abstract = {A general class of linear time invariant systems with time delay is studied. Recently, they attracted considerable interest in the systems and control community. The complexity arises due to the exponential type transcendental terms in their characteristic equation. The transcendentality brings infinitely many characteristic roots, which are cumbersome to elaborate as evident from the literature. A number of methodologies have been suggested with limited ability to assess the stability in the parametric domain of time delay. This study offers an exact, structured and robust methodology to bring a closure to the question at hand. Ultimately we present a unique explicit analytical expression in terms of the system parameters which not only reveals the stability regions (pockets) in the domain of time delay, but it also declares the number of unstable characteristic roots at any given pocket. The method starts with the determination of all possible purely imaginary (resonant) characteristic roots for any positive time delay. To achieve this a simplifying substitution is used for the transcendental terms in the characteristic equation. It is proven that the number of such resonant roots for a given dynamics is finite. Each one of these roots is created by infinitely many time delays, which are periodically distributed. An interesting property is also claimed next, that the root crossing directions at these locations are invariant with respect to the delay and dependent only on the crossing frequency. These two unique findings facilitate a simple and practical stability method, which is the highlight of the work.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5GZXFUIR\\Olgac and Sipahi - 2002 - An exact method for the stability analysis of time.pdf}
}

@book{oliveira_1984,
  title = {Statistical {{Extremes}} and {{Applications}}},
  author = {Oliveira, J. Tiago},
  year = {1984},
  publisher = {{Springer Netherlands}},
  address = {{Dordrecht}},
  abstract = {The first references to statistical extremes may perhaps be found in the Genesis (The Bible, vol. I): the largest age of Methu'selah and the concrete applications faced by Noah-- the long rain, the large flood, the structural safety of the ark --. But as the pre-history of the area can be considered to last to the first quarter of our century, we can say that Statistical Extremes emer ged in the last half-century. It began with the paper by Dodd in 1923, followed quickly by the papers of Fre-chet in 1927 and Fisher and Tippett in 1928, after by the papers by de Finetti in 1932, by Gumbel in 1935 and by von Mises in 1936, to cite the more relevant; the first complete frame in what regards probabilistic problems is due to Gnedenko in 1943. And by that time Extremes begin to explode not only in what regards applications (floods, breaking strength of materials, gusts of wind, etc.) but also in areas going from Proba bility to Stochastic Processes, from Multivariate Structures to Statistical Decision. The history, after the first essential steps, can't be written in few pages: the narrow and shallow stream gained momentum and is now a huge river, enlarging at every moment and flooding the margins. Statistical Extremes is, thus, a clear-cut field of Probability and Statistics and a new exploding area for research.},
  isbn = {978-94-017-3069-3},
  langid = {english},
  annotation = {00055  OCLC: 851392496},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\S2QRECHQ\\Oliveira - 1984 - Statistical Extremes and Applications.pdf}
}

@article{olszowy_2019,
  title = {Accurate Autocorrelation Modeling Substantially Improves {{fMRI}} Reliability},
  author = {Olszowy, Wiktor and Aston, John and Rua, Catarina and Williams, Guy B.},
  year = {2019},
  month = dec,
  journal = {Nature Communications},
  volume = {10},
  number = {1},
  issn = {2041-1723},
  doi = {10.1038/s41467-019-09230-w},
  langid = {english},
  annotation = {00005},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\J36E3L33\\Olszowy et al. - 2019 - Accurate autocorrelation modeling substantially im.pdf}
}

@article{oneill_2003,
  title = {Some Limits of Informed Consent},
  author = {O'Neill, Onora},
  year = {2003},
  journal = {Journal of medical ethics},
  volume = {29},
  number = {1},
  pages = {4--7},
  annotation = {00426},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7NG23AI3\\O'Neill 2003 - Some limits of informed consent.pdf;C\:\\Users\\dirko\\Zotero\\storage\\7PYI6S75\\O'Neill - 2003 - Some limits of informed consent.pdf;C\:\\Users\\dirko\\Zotero\\storage\\H9V33FK6\\O'Neill - 2003 - Some limits of informed consent.pdf}
}

@article{opitz_2002,
  title = {Differential {{Contribution}} of {{Frontal}} and {{Temporal Cortices}} to {{Auditory Change Detection}}: {{fMRI}} and {{ERP Results}}},
  shorttitle = {Differential {{Contribution}} of {{Frontal}} and {{Temporal Cortices}} to {{Auditory Change Detection}}},
  author = {Opitz, Bertram and Rinne, Teemu and Mecklinger, Axel and {von Cramon}, D.Yves and Schr{\"o}ger, Erich},
  year = {2002},
  month = jan,
  journal = {NeuroImage},
  volume = {15},
  number = {1},
  pages = {167--174},
  issn = {10538119},
  doi = {10.1006/nimg.2001.0970},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\L8YIUPR7\\Opitz et al_2002_NeuroImage_Differential Contribution of Frontal and Temporal Cortices to Auditory Change.pdf}
}

@book{oppenheim_2010,
  title = {Discrete-Time Signal Processing},
  author = {Oppenheim, Alan V. and Schafer, Ronald W.},
  year = {2010},
  edition = {3rd ed},
  publisher = {{Pearson}},
  address = {{Upper Saddle River}},
  isbn = {978-0-13-198842-2},
  langid = {english},
  lccn = {TK5102.9 .O67 2010},
  keywords = {Discrete-time systems,Mathematics,Signal processing},
  annotation = {27341},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KQF4RVMK\\Oppenheim und Schafer - 2010 - Discrete-time signal processing.pdf}
}

@article{ormerod_2010,
  title = {Explaining {{Variational Approximations}}},
  author = {Ormerod, J. T. and Wand, M. P.},
  year = {2010},
  month = may,
  journal = {The American Statistician},
  volume = {64},
  number = {2},
  pages = {140--153},
  issn = {0003-1305, 1537-2731},
  doi = {10.1198/tast.2010.09058},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZX842HSJ\\Ormerod und Wand - 2010 - Explaining Variational Approximations.pdf}
}

@article{osborne_1992,
  ids = {osborne_fisher_1992a},
  title = {Fisher's {{Method}} of {{Scoring}}},
  author = {Osborne, M. R.},
  year = {1992},
  month = apr,
  journal = {International Statistical Review / Revue Internationale de Statistique},
  volume = {60},
  number = {1},
  pages = {99},
  issn = {03067734},
  doi = {10.2307/1403504},
  abstract = {An analysis is given of the computational properties of Fisher's method of scoring for maximizing likelihoods and solving estimating equations based on quasi-likelihoods. Consistent estimation of the true parameter vector is shown to be important if a fast rate of convergence is to be achieved, but if this condition is met then the algorithm is very attractive. This link between the performance of the scoring algorithm and the adequacy of the underlying problem modelling is stressed. The effect of linear constraints on performance is discussed, and examples of likelihood and quasi-likelihood calculations are presented.},
  langid = {english},
  annotation = {00091},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Q92QUSEH\\Osborne - 1992 - Fisher's Method of Scoring.pdf}
}

@article{ostwald_2006,
  title = {Effects on {{Hippocampus}} of {{Lifelong Absence}} of {{Glucocorticoids}} in the {{Pro-Opiomelanocortin Null Mutant Mouse Reveal Complex Relationship Between Glucocorticoids}} and {{Hippocampal Structure}} and {{Function}}},
  author = {Ostwald, Dirk and Karpac, Jason and Hochgeschwender, Ute},
  year = {2006},
  journal = {Journal of Molecular Neuroscience},
  volume = {28},
  number = {3},
  pages = {291--302},
  issn = {0895-8696},
  doi = {10.1385/JMN:28:3:291},
  abstract = {In humans changes in serum cortisol levels have been observed with aging, stress, and with affective disorders such as major depression and post-traumatic stress disorder. Corticosteroids are known to influence hippocampal structure and function; specifically, plasma corticosteroid levels have been inversely correlated with hippocampal cell proliferation, cell death, and impaired memory function. The relationship between corticosteroids and structure and function of the hippocampus has been studied in experimental systems in adult animals by increasing or decreasing corticosterone levels through pharmacological supplementation and through surgical removal of the adrenal gland. Here, we utilized the genetically engineered pro-opiomelanocortin (POMC) null mutant mouse, which because of the lack of all POMC peptides has no corticosterone from birth throughout life. The effect of this lifelong absence of corticosterone on the dentate gyrus of the hippocampus is a decrease in granule cell density, which correlated with a decrease in cell proliferation but not an increase in cell degeneration. Fine morphology of granule cells was unaltered. Analyses of gene expression revealed no changes in POMC null mutant vs wild-type hippocampus with respect to levels of expression of corticoid receptor genes or genes known to be regulated by corticosterone. Spatial learning as tested by the Morris water maze was not altered in the POMC null mutant mouse. Taken together with findings from other studies of the effects of altered levels of corticosteroids on the hippocampus, our results argue for a complex homeostasis in which disturbances of any one factor can offset the system in varying ways.},
  langid = {english},
  annotation = {00004},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\H5ZRSZMX\\Ostwald et al. - 2006 - Effects on Hippocampus of Lifelong Absence of Gluc.pdf}
}

@article{ostwald_2008,
  title = {Neural {{Coding}} of {{Global Form}} in the {{Human Visual Cortex}}},
  author = {Ostwald, Dirk and Lam, Judith M. and Li, Sheng and Kourtzi, Zoe},
  year = {2008},
  month = may,
  journal = {Journal of Neurophysiology},
  volume = {99},
  number = {5},
  pages = {2456--2469},
  issn = {0022-3077, 1522-1598},
  doi = {10.1152/jn.01307.2007},
  langid = {english},
  annotation = {00098},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HLZ75PKB\\Ostwald et al. - 2008 - Neural Coding of Global Form in the Human Visual C.pdf}
}

@article{ostwald_2010,
  title = {An Information Theoretic Approach to {{EEG}}\textendash{{fMRI}} Integration of Visually Evoked Responses},
  author = {Ostwald, Dirk and Porcaro, Camillo and Bagshaw, Andrew P.},
  year = {2010},
  month = jan,
  journal = {NeuroImage},
  volume = {49},
  number = {1},
  pages = {498--516},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.07.038},
  abstract = {The integration of signals from electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI), acquired simultaneously from the same observer, holds great potential for the elucidation of the neurobiological underpinnings of human brain function. However, the most appropriate way in which to combine the data in order to achieve this goal is not clear. Here, we apply a novel route to the integration of simultaneously acquired multimodal brain imaging data. We adopt a theoretical framework developed in the study of neuronal population codes which explicitly takes into account the experimentally observed stimulus\textendash response signal probability distributions using the concept of mutual information. We study the implications of this framework using simulated data sets generated from a set of linear Gaussian models, and apply the framework to EEG\textendash fMRI data acquired during checkerboard stimulation of low and high contrast. We focus our evaluation on single-trial time-domain signal features from both modalities and provide evidence for the informativeness of a subset of these features with respect to the stimulus and each other. Specifically, the framework was able to identify the contrast dependency of the haemodynamic response and the P100 peak of the visual evoked potential, and showed that combining EEG and fMRI time-domain features by quantifying the information in their joint distribution was more informative than treating each one in isolation. In addition, the effect of different pre-processing strategies for EEG-fMRI data can be assessed quantitatively, indicating the improvements to be gained by more advanced methods. We conclude that the information theoretic framework is a promising methodology to quantify the relative importance of different response features in neural coding and neurovascular coupling, as well as the success of data pre-processing strategies.},
  langid = {english},
  annotation = {00054},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\AB8E5PXR\\Ostwald et al. - 2010 - An information theoretic approach to EEG–fMRI inte.pdf}
}

@article{ostwald_2011,
  title = {Information Theoretic Approaches to Functional Neuroimaging},
  author = {Ostwald, Dirk and Bagshaw, Andrew P.},
  year = {2011},
  month = dec,
  journal = {Magnetic Resonance Imaging},
  volume = {29},
  number = {10},
  pages = {1417--1428},
  issn = {0730725X},
  doi = {10.1016/j.mri.2011.07.013},
  abstract = {Information theory is a probabilistic framework that allows the quantification of statistical non-independence between signals of interest. In contrast to other methods used for this purpose, it is model free, i.e., it makes no assumption about the functional form of the statistical dependence or the underlying probability distributions. It thus has the potential to unveil important signal characteristics overlooked by classical data analysis techniques. In this review, we discuss how information theoretic concepts have been applied to the analysis of functional brain imaging data such as functional magnetic resonance imaging and magneto/electroencephalography. We review studies from a number of imaging domains, including the investigation of the brain's functional specialization and integration, neurovascular coupling and multimodal imaging. We demonstrate how information theoretical concepts can be used to answer neurobiological questions and discuss their limitations as well as possible future developments of the framework to advance our understanding of brain function.},
  langid = {english},
  annotation = {00015},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZECV4BUG\\Ostwald and Bagshaw - 2011 - Information theoretic approaches to functional neu.pdf}
}

@article{ostwald_2011a,
  title = {Voxel-Wise Information Theoretic {{EEG-fMRI}} Feature Integration},
  author = {Ostwald, Dirk and Porcaro, Camillo and Bagshaw, Andrew P.},
  year = {2011},
  month = apr,
  journal = {NeuroImage},
  volume = {55},
  number = {3},
  pages = {1270--1286},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2010.12.029},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FMBWUYQI\\Ostwald et al. - 2011 - Voxel-wise information theoretic EEG-fMRI feature .pdf}
}

@article{ostwald_2012,
  title = {{{EEG-fMRI Based Information Theoretic Characterization}} of the {{Human Perceptual Decision System}}},
  author = {Ostwald, Dirk and Porcaro, Camillo and Mayhew, Stephen D. and Bagshaw, Andrew P.},
  editor = {Kiebel, Stefan J.},
  year = {2012},
  month = apr,
  journal = {PLoS ONE},
  volume = {7},
  number = {4},
  pages = {e33896},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0033896},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZSMYWIRL\\Ostwald et al. - 2012 - EEG-fMRI Based Information Theoretic Characterizat.pdf}
}

@article{ostwald_2012a,
  ids = {ostwald2012},
  title = {Evidence for Neural Encoding of {{Bayesian}} Surprise in Human Somatosensation},
  author = {Ostwald, Dirk and Spitzer, Bernhard and Guggenmos, Matthias and Schmidt, Timo T. and Kiebel, Stefan J. and Blankenburg, Felix},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {1},
  pages = {177--188},
  publisher = {{Elsevier}},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.04.050},
  abstract = {Accumulating empirical evidence suggests a role of Bayesian inference and learning for shaping neural responses in auditory and visual perception. However, its relevance for somatosensory processing is unclear. In the present study we test the hypothesis that cortical somatosensory processing exhibits dynamics that are consistent with Bayesian accounts of brain function. Specifically, we investigate the cortical encoding of Bayesian surprise, a recently proposed marker of Bayesian perceptual learning, using EEG data recorded from 15 subjects. Capitalizing on a somatosensory mismatch roving paradigm, we performed computational single-trial modeling of evoked somatosensory potentials for the entire peri-stimulus time period in source space. By means of Bayesian model selection, we find that, at 140 ms post-stimulus onset, secondary somatosensory cortex represents Bayesian surprise rather than stimulus change, which is the conventional marker of EEG mismatch responses. In contrast, at 250 ms, right inferior frontal cortex indexes stimulus change. Finally, at 360 ms, our analyses indicate additional perceptual learning attributable to medial cingulate cortex. In summary, the present study provides novel evidence for anatomical-temporal/functional segregation in human somatosensory processing that is consistent with the Bayesian brain hypothesis.},
  langid = {english},
  annotation = {00034},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NR4PEK5F\\Ostwald et al. - 2012 - Evidence for neural encoding of Bayesian surprise .pdf}
}

@article{ostwald_2014,
  ids = {ostwald2014},
  title = {A Tutorial on Variational {{Bayes}} for Latent Linear Stochastic Time-Series Models},
  author = {Ostwald, Dirk and Kirilina, Evgeniya and Starke, Ludger and Blankenburg, Felix},
  year = {2014},
  month = jun,
  journal = {Journal of Mathematical Psychology},
  volume = {60},
  pages = {1--19},
  publisher = {{Elsevier}},
  issn = {00222496},
  doi = {10.1016/j.jmp.2014.04.003},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Z5I64PQN\\Ostwald et al_2014_Journal of Mathematical Psychology_A tutorial on variational Bayes for latent linear stochastic time-series models.pdf}
}

@article{ostwald_2015,
  title = {A Normative Inference Approach for Optimal Sample Sizes in Decisions from Experience},
  author = {Ostwald, Dirk and Starke, Ludger and Hertwig, Ralph},
  year = {2015},
  month = sep,
  journal = {Frontiers in Psychology},
  volume = {6},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2015.01342},
  abstract = {``Decisions from experience'' (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the ``sampling paradigm,'' which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the ``optimal'' sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE.},
  annotation = {00002},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\49UBWKCB\\Ostwald et al. - 2015 - A normative inference approach for optimal sample .pdf}
}

@misc{ostwald_2015a,
  title = {Interdisciplinary {{College}} 2015 - {{The Free Energy Principle}} for {{Perception}}},
  author = {Ostwald, Dirk},
  year = {2015},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MZJVSE88\\IK_2015_Course_Notes.pdf}
}

@article{ostwald_2016,
  title = {Probabilistic Delay Differential Equation Modeling of Event-Related Potentials},
  author = {Ostwald, Dirk and Starke, Ludger},
  year = {2016},
  month = aug,
  journal = {NeuroImage},
  volume = {136},
  pages = {227--257},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2016.04.025},
  abstract = {Dynamic causal models'' (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-DCM combines deterministic dynamical neural mass models with dipole-based EEG forward models to describe the event-related scalp potential time-series over the entire electrode space. Since its inception, ERP-DCM has been successfully employed to capture the neural underpinnings of a wide range of neurocognitive phenomena. However, in spite of its empirical popularity, the technical literature on ERP-DCM remains somewhat patchy. A number of previous communications have detailed certain aspects of the approach, but no unified and coherent documentation exists. With this technical note, we aim to close this gap and to increase the technical accessibility of ERP-DCM. Specifically, this note makes the following novel contributions: firstly, we provide a unified and coherent review of the mathematical machinery of the latent and forward models constituting ERP-DCM by formulating the approach as a probabilistic latent delay differential equation model. Secondly, we emphasize the probabilistic nature of the model and its variational Bayesian inversion scheme by explicitly deriving the variational free energy function in terms of both the likelihood expectation and variance parameters. Thirdly, we detail and validate the estimation of the model with a special focus on the explicit form of the variational free energy function and introduce a conventional nonlinear optimization scheme for its maximization. Finally, we identify and discuss a number of computational issues which may be addressed in the future development of the approach.},
  langid = {english},
  annotation = {00004},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RG8Y7VWZ\\Ostwald and Starke - 2016 - Probabilistic delay differential equation modeling.pdf}
}

@inproceedings{ostwald_2018,
  title = {Computational Mechanisms of Human State-Action-Reward Contingency Learning under Perceptual Uncertainty},
  booktitle = {2018 {{Conference}} on {{Cognitive Computational Neuroscience}}},
  author = {Ostwald, Dirk and Brucker, Rasmus and Heekeren, Hauke},
  year = {2018},
  annotation = {00003}
}

@article{ostwald_2018a,
  title = {Random Field Theory-Based p-Values: A Review of the {{SPM}} Implementation},
  shorttitle = {Random Field Theory-Based p-Values},
  author = {Ostwald, Dirk and Schneider, Sebastian and Bruckner, Rasmus and Horvath, Lilla},
  year = {2018},
  journal = {arXiv:1808.04075 [q-bio]},
  eprint = {1808.04075},
  eprinttype = {arxiv},
  primaryclass = {q-bio},
  abstract = {P-values and null-hypothesis significance testing are popular data-analytical tools in functional neuroimaging. Sparked by the analysis of resting-state fMRI data, there has recently been a resurgence of interest in the validity of some of the p-values evaluated with the widely used software SPM. The default parametric p-values reported in SPM are based on the application of results from random field theory to statistical parametric maps, a framework we refer to as RFP. While RFP was established almost two decades ago and has since been applied in a plethora of fMRI studies, there does not exist a unified documentation of the mathematical and computational underpinnings of RFP as implemented in current versions of SPM. Here, we provide such a documentation with the aim of contributing to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Quantitative Biology - Quantitative Methods},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XP64N8LC\\Ostwald et al. - 2019 - Random field theory-based p-values a review of th.pdf}
}

@inproceedings{ostwald_2018b,
  title = {The Neurocomputational Mechanisms of Human Sequential Decision Making under Uncertainty in a Spatial Search Task},
  booktitle = {2018 {{Conference}} on {{Cognitive Computational Neuroscience}}},
  author = {Ostwald, Dirk},
  year = {2018},
  annotation = {00000}
}

@article{ostwald_2019,
  ids = {ostwald_power_2019a},
  title = {Power, Positive Predictive Value, and Sample Size Calculations for Random Field Theory-Based {{fMRI}} Inference},
  author = {Ostwald, Dirk and Schneider, Sebastian and Bruckner, Rasmus and Horvath, Lilla},
  year = {2019},
  month = apr,
  journal = {BioRxiv: doi.org/10.1101/613331},
  doi = {10.1101/613331},
  abstract = {Recent discussions on the reproducibility of task-related functional magnetic resonance imaging (fMRI) studies have emphasized the importance of power and sample size calculations in fMRI study planning. In general, statistical power and sample size calculations are dependent on the statistical inference framework that is used to test hypotheses. Bibliometric analyses suggest that random  eld theory (RFT)-based voxel- and cluster-level fMRI inference are the most commonly used approaches for the statistical evaluation of task-related fMRI data. However, general power and sample size calculations for these inference approaches remain elusive. Based on the mathematical theory of RFT-based inference, we here develop power and positive predictive value (PPV) functions for voxel- and cluster-level inference in both uncorrected single test and corrected multiple testing scenarios. Moreover, we apply the theoretical results to evaluate the sample size necessary to achieve desired power and PPV levels based on an fMRI pilot study.},
  langid = {english},
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}

@misc{ostwald_2019a,
  title = {Statistics for {{Data Science}} 2019/20 - (14) {{Numerical Methods}}. {{Available}} from {{https://www.youtube.com/watch?v=G74jJMmHemA\&list=PLhzkDK3o0fcDE3V\_3r04isntPggpAK-Dp\&index=17\&t=8s}}},
  author = {Ostwald, Dirk},
  year = {2019}
}

@article{ostwald_2021,
  title = {An Induction Proof of the Backpropagation Algorithm in Matrix Notation},
  author = {Ostwald, Dirk and Us{\'e}e, Franziska},
  year = {2021},
  month = jul,
  journal = {arXiv:2107.09384 [cs, math, q-bio, stat]},
  eprint = {2107.09384},
  eprinttype = {arxiv},
  primaryclass = {cs, math, q-bio, stat},
  abstract = {Backpropagation (BP) is a core component of the contemporary deep learning incarnation of neural networks. Briefly, BP is an algorithm that exploits the computational architecture of neural networks to efficiently evaluate the gradient of a cost function during neural network parameter optimization. The validity of BP rests on the application of a multivariate chain rule to the computational architecture of neural networks and their associated objective functions. Introductions to deep learning theory commonly present the computational architecture of neural networks in matrix form, but eschew a parallel formulation and justification of BP in the framework of matrix differential calculus. This entails several drawbacks for the theory and didactics of deep learning. In this work, we overcome these limitations by providing a full induction proof of the BP algorithm in matrix notation. Specifically, we situate the BP algorithm in the framework of matrix differential calculus, encompass affine-linear potential functions, prove the validity of the BP algorithm in inductive form, and exemplify the implementation of the matrix form BP algorithm in computer code.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Mathematics - Statistics Theory,Quantitative Biology - Neurons and Cognition,Statistics - Machine Learning},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7EGBAY8L\\Ostwald und Usée - 2021 - An induction proof of the backpropagation algorith.pdf}
}

@book{palanisamy_2018,
  title = {Hands-on Intelligent Agents with {{OpenAI Gym}}: A Step-by-Step Guide to Develop {{AI}} Agents Using Deep Reinforcement Learning},
  shorttitle = {Hands-on Intelligent Agents with {{OpenAI Gym}}},
  author = {Palanisamy, Praveen},
  year = {2018},
  abstract = {Walks through the hands-on process of building intelligent agents from the basics and all the way up to solving complex problems including playing Atari games and driving a car autonomously in the CARLA simulator. Discusses various learning environments and how to transform real-world problems into learning environments and solve using the agents.},
  isbn = {978-1-78883-513-8},
  langid = {english},
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}

@article{palmeri_2019,
  title = {On {{Testing}} and {{Developing Cognitive Models}}},
  author = {Palmeri, Thomas J.},
  year = {2019},
  month = dec,
  journal = {Computational Brain \& Behavior},
  volume = {2},
  number = {3-4},
  pages = {193--196},
  issn = {2522-0861, 2522-087X},
  doi = {10.1007/s42113-019-00041-2},
  abstract = {The target article, ``Robust Modeling in Cognitive Science,'' proposes a number of recommended practices in computational modeling in response to the growing ``crisis of confidence'' facing many scientific disciplines, including psychology and neuroscience. Those of us who do modeling, write about modeling, teach modeling, and mentor modelers worry deeply about best practices and any new suggestions for making modeling more transparent, trusted, and robust are welcome. Many of the recommendations seem uncontroversial. My commentary focuses on forms of preregistration and postregistration, which constitute three of the four key ideas highlighted as take-home recommendations at the conclusion of the target article. I have chosen to consider these recommendations by reflecting on my own past experiences developing new models and modeling approaches.},
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@article{palminteri_2017,
  title = {The {{Importance}} of {{Falsification}} in {{Computational Cognitive Modeling}}},
  author = {Palminteri, Stefano and Wyart, Valentin and Koechlin, Etienne},
  year = {2017},
  month = jun,
  journal = {Trends in Cognitive Sciences},
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@article{pantazis_2005,
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@article{park_2012,
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  year = {2012},
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@article{parzen_1962,
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@article{passingham_2002,
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@article{pearson_1900,
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@article{pearson_1901,
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@article{peirce_2007,
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  title = {{{PsychoPy}}\textemdash{{Psychophysics}} Software in {{Python}}},
  author = {Peirce, Jonathan W.},
  year = {2007},
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  journal = {Journal of Neuroscience Methods},
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  pages = {8--13},
  publisher = {{Elsevier}},
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  doi = {10.1016/j.jneumeth.2006.11.017},
  abstract = {The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment.},
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}

@article{peirce_2019,
  title = {{{PsychoPy2}}: {{Experiments}} in Behavior Made Easy},
  shorttitle = {{{PsychoPy2}}},
  author = {Peirce, Jonathan and Gray, Jeremy R. and Simpson, Sol and MacAskill, Michael and H{\"o}chenberger, Richard and Sogo, Hiroyuki and Kastman, Erik and Lindel{\o}v, Jonas Kristoffer},
  year = {2019},
  month = feb,
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  volume = {51},
  number = {1},
  pages = {195--203},
  issn = {1554-3528},
  doi = {10.3758/s13428-018-01193-y},
  abstract = {PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.},
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}

@book{pena_2001,
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  editor = {Pe{\~n}a, Daniel and Tiao, George C. and Tsay, Ruey S.},
  year = {2001},
  series = {Wiley Series in Probability and Statistics},
  publisher = {{J. Wiley}},
  address = {{New York}},
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  lccn = {QA280 .C66 2001},
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}

@article{penny_,
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  author = {Penny, Will},
  pages = {234},
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}

@article{penny_2003,
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  author = {Penny, Will and Kiebel, Stefan and Friston, Karl},
  year = {2003},
  month = jul,
  journal = {NeuroImage},
  volume = {19},
  number = {3},
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  issn = {10538119},
  doi = {10.1016/S1053-8119(03)00071-5},
  abstract = {We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density. The fidelity of this approximation is verified via Gibbs sampling. The VB approach provides a natural extension to previous Bayesian analyses which have used Empirical Bayes. VB has the advantage of taking into account the variability of hyperparameter estimates with little additional computational effort. Further, VB allows for automatic selection of the order of the AR process. Results are shown on simulated data and on data from an event-related fMRI experiment.},
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}

@article{penny_2005,
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  author = {Penny, William D. and {Trujillo-Barreto}, Nelson J. and Friston, Karl J.},
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@article{penny_2007,
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  author = {Penny, W.D. and Kilner, J. and Blankenburg, F.},
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@article{penny_2012,
  title = {Comparing {{Dynamic Causal Models}} Using {{AIC}}, {{BIC}} and {{Free Energy}}},
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@article{pereira_2017,
  title = {Attractor Dynamics in Networks with Learning Rules Inferred from in Vivo Data},
  author = {Pereira, Ulises and Brunel, Nicolas},
  year = {2017},
  month = oct,
  doi = {10.1101/199521},
  abstract = {The attractor neural network scenario is a popular scenario for memory storage in the association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both learning rules and distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in the inferior temporal cortex (ITC). Unlike classical attractor neural network models, our model exhibits graded activity in retrieval states, with distributions of firing rates that are close to lognormal. Inferred learning rules are close to maximizing the number of stored patterns within a family of unsupervised Hebbian learning rules, suggesting that learning rules in ITC are optimized to store a large number of attractor states. Finally, we show that there exist two types of retrieval states: one in which firing rates are constant in time and another in which firing rates fluctuate chaotically.},
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@article{pereira_2018,
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  abstract = {Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sentences with topics not encountered during training.},
  copyright = {2018 The Author(s)},
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@article{pernet_2019,
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@article{pernet_2020,
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@book{pesarin_2010,
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@inproceedings{petersen_2005,
  title = {The {{EM Algorithm}} in {{Independent Component Analysis}}},
  booktitle = {Proceedings. ({{ICASSP}} '05). {{IEEE International Conference}} on {{Acoustics}}, {{Speech}}, and {{Signal Processing}}, 2005.},
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  pages = {169--172},
  publisher = {{IEEE}},
  address = {{Philadelphia, Pennsylvania, USA}},
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  abstract = {We investigate two techniques for Independent Component Analysis which use the Expectation-Maximization algorithm. Analysis and simulations show that convergence becomes extraordinary slow for almost all cases, compared to other optimization techniques. The two alternatives considered are ''Adaptive Overrelaxed EM'' and Ucminf (a BFGS with soft line search), which both improves the convergence dramatically with little or no extra analytical work. We discuss the generality and perspectives of the findings.},
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@article{petersen_2012,
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@article{petersson_,
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@article{petrini_2010,
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@article{phillips_2002,
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@article{pier_2018,
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@article{pillai_1955,
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\YB4WLEI7\\Pillai - 1955 - Some New Test Criteria in Multivariate Analysis.pdf}
}

@article{pilly_2009,
  title = {What a Difference a Parameter Makes: {{A}} Psychophysical Comparison of Random Dot Motion Algorithms},
  shorttitle = {What a Difference a Parameter Makes},
  author = {Pilly, Praveen K. and Seitz, Aaron R.},
  year = {2009},
  month = jul,
  journal = {Vision Research},
  volume = {49},
  number = {13},
  pages = {1599--1612},
  issn = {00426989},
  doi = {10.1016/j.visres.2009.03.019},
  abstract = {Random dot motion (RDM) displays have emerged as one of the standard stimulus types employed in psychophysical and physiological studies of motion processing. RDMs are convenient because it is straightforward to manipulate the relative motion energy for a given motion direction in addition to stimulus parameters such as the speed, contrast, duration, density, aperture, etc. However, as widely as RDMs are employed so do they vary in their details of implementation. As a result, it is often difficult to make direct comparisons across studies employing different RDM algorithms and parameters. Here, we systematically measure the ability of human subjects to estimate motion direction for four commonly used RDM algorithms under a range of parameters in order to understand how these different algorithms compare in their perceptibility. We find that parametric and algorithmic differences can produce dramatically different performances. These effects, while surprising, can be understood in relationship to pertinent neurophysiological data regarding spatiotemporal displacement tuning properties of cells in area MT and how the tuning function changes with stimulus contrast and retinal eccentricity. These data help give a baseline by which different RDM algorithms can be compared, demonstrate a need for clearly reporting RDM details in the methods of papers, and also pose new constraints and challenges to models of motion direction processing.},
  langid = {english},
  annotation = {00071},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7JTS4RTW\\Pilly und Seitz - 2009 - What a difference a parameter makes A psychophysi.pdf}
}

@article{pinkus_1999,
  title = {Approximation Theory of the {{MLP}} Model in Neural Networks},
  author = {Pinkus, Allan},
  year = {1999},
  month = jan,
  journal = {Acta Numerica},
  volume = {8},
  pages = {143--195},
  issn = {0962-4929, 1474-0508},
  doi = {10.1017/S0962492900002919},
  abstract = {In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models. Mathematically it is also one of the simpler models. Nonetheless the mathematics of this model is not well understood, and many of these problems are approximation-theoretic in character. Most of the research we will discuss is of very recent vintage. We will report on what has been done and on various unanswered questions. We will not be presenting practical (algorithmic) methods. We will, however, be exploring the capabilities and limitations of this model.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EI5CHZPG\\Pinkus - 1999 - Approximation theory of the MLP model in neural ne.pdf}
}

@article{pinotsis_2013,
  title = {Dynamic Causal Modelling of Lateral Interactions in the Visual Cortex},
  author = {Pinotsis, D.A. and Schwarzkopf, D.S. and Litvak, V. and Rees, G. and Barnes, G. and Friston, K.J.},
  year = {2013},
  month = feb,
  journal = {NeuroImage},
  volume = {66},
  pages = {563--576},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.10.078},
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}

@book{pinter_2010,
  title = {A Book of Abstract Algebra},
  author = {Pinter, Charles C.},
  year = {2010},
  edition = {2. ed},
  publisher = {{Dover Publ}},
  address = {{Mineola, NY}},
  isbn = {978-0-486-47417-5},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6NFCPXQL\\Pinter - 2010 - A book of abstract algebra.pdf}
}

@book{pinter_2010a,
  title = {A Book of Abstract Algebra},
  author = {Pinter, Charles C.},
  year = {2010},
  edition = {2. ed},
  publisher = {{Dover Publ}},
  address = {{Mineola, NY}},
  isbn = {978-0-486-47417-5},
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}

@article{pitt_2013,
  title = {What {{Should Be}} the {{Data Sharing Policy}} of {{Cognitive Science}}?},
  author = {Pitt, Mark A. and Tang, Yun},
  year = {2013},
  month = jan,
  journal = {Topics in Cognitive Science},
  volume = {5},
  number = {1},
  pages = {214--221},
  issn = {17568757},
  doi = {10.1111/tops.12006},
  abstract = {There is a growing chorus of voices in the scientific community calling for greater openness in the sharing of raw data that lead to a publication. In this commentary, we discuss the merits of sharing, common concerns that are raised, and practical issues that arise in developing a sharing policy. We suggest that the cognitive science community discuss the topic and establish a data-sharing policy.},
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}

@article{pleger_2006,
  title = {Repetitive {{Transcranial Magnetic Stimulation-Induced Changes}} in {{Sensorimotor Coupling Parallel Improvements}} of {{Somatosensation}} in {{Humans}}},
  author = {Pleger, B.},
  year = {2006},
  month = feb,
  journal = {Journal of Neuroscience},
  volume = {26},
  number = {7},
  pages = {1945--1952},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.4097-05.2006},
  langid = {english},
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}

@article{polack_2020,
  title = {Safety and {{Efficacy}} of the {{BNT162b2 mRNA Covid-19 Vaccine}}},
  author = {Polack, Fernando P and Thomas, Stephen J and Kitchin, Nicholas and Absalon, Judith and Gurtman, Alejandra and Lockhart, Stephen and Perez, John L and Marc, Gonzalo P{\'e}rez and Moreira, Edson D and Zerbini, Cristiano and Bailey, Ruth and Swanson, Kena A and Roychoudhury, Satrajit and Koury, Kenneth and Li, Ping and Kalina, Warren V and Cooper, David and Frenck, Robert W},
  year = {2020},
  journal = {n engl j med},
  pages = {13},
  abstract = {BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the resulting coronavirus disease 2019 (Covid-19) have afflicted tens of millions of people in a worldwide pandemic. Safe and effective vaccines are needed urgently. METHODS In an ongoing multinational, placebo-controlled, observer-blinded, pivotal efficacy trial, we randomly assigned persons 16 years of age or older in a 1:1 ratio to receive two doses, 21 days apart, of either placebo or the BNT162b2 vaccine candidate (30 {$\mu$}g per dose). BNT162b2 is a lipid nanoparticle\textendash formulated, nucleoside-modified RNA vaccine that encodes a prefusion stabilized, membrane-anchored SARS-CoV-2 fulllength spike protein. The primary end points were efficacy of the vaccine against laboratory-confirmed Covid-19 and safety. The authors' affiliations are listed in the Appendix. Address reprint requests to Dr. Absalon at Pfizer, 401 N. Middletown Rd., Pearl River, NY 10965, or at \-judith .\-absalon@\-pfizer.\-com. *A complete list of investigators in the C4591001 Clinical Trial Group is provided in the Supplementary Appendix, available at NEJM.org. Drs. Polack and Thomas contributed equally to this article. This article was published on December 10, 2020, at NEJM.org. RESULTS A total of 43,548 participants underwent randomization, of whom 43,448 received injections: 21,720 with BNT162b2 and 21,728 with placebo. There were 8 cases of Covid-19 with onset at least 7 days after the second dose among participants assigned to receive BNT162b2 and 162 cases among those assigned to placebo; BNT162b2 was 95\% effective in preventing Covid-19 (95\% credible interval, 90.3 to 97.6). Similar vaccine efficacy (generally 90 to 100\%) was observed across subgroups defined by age, sex, race, ethnicity, baseline body-mass index, and the presence of coexisting conditions. Among 10 cases of severe Covid-19 with onset after the first dose, 9 occurred in placebo recipients and 1 in a BNT162b2 recipient. The safety profile of BNT162b2 was characterized by short-term, mild-to-moderate pain at the injection site, fatigue, and headache. The incidence of serious adverse events was low and was similar in the vaccine and placebo groups. DOI: 10.1056/NEJMoa2034577 Copyright \textcopyright{} 2020 Massachusetts Medical Society. CONCLUSIONS A two-dose regimen of BNT162b2 conferred 95\% protection against Covid-19 in persons 16 years of age or older. Safety over a median of 2 months was similar to that of other viral vaccines. (Funded by BioNTech and Pfizer; ClinicalTrials.gov number, NCT04368728.)},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KUUIF3PV\\Polack et al. - 2020 - Safety and Efficacy of the BNT162b2 mRNA Covid-19 .pdf}
}

@article{poldrack_2006,
  title = {Can Cognitive Processes Be Inferred from Neuroimaging Data?},
  author = {Poldrack, R},
  year = {2006},
  month = feb,
  journal = {Trends in Cognitive Sciences},
  volume = {10},
  number = {2},
  pages = {59--63},
  issn = {13646613},
  doi = {10.1016/j.tics.2005.12.004},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8HGTW9E4\\Poldrack_2006_Trends in Cognitive Sciences_Can cognitive processes be inferred from neuroimaging data.pdf}
}

@book{poldrack_2011,
  title = {Handbook of {{Functional MRI Data Analysis}}},
  author = {Poldrack, Russell A. and Nichols, Thomas and Mumford, Jeanette},
  year = {2011},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge}},
  doi = {10.1017/CBO9780511895029},
  isbn = {978-0-511-89502-9},
  langid = {english},
  annotation = {00367},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PG53G9RD\\Poldrack et al. - 2011 - Handbook of Functional MRI Data Analysis.pdf}
}

@article{poldrack_2013,
  title = {Toward Open Sharing of Task-Based {{fMRI}} Data: The {{OpenfMRI}} Project},
  shorttitle = {Toward Open Sharing of Task-Based {{fMRI}} Data},
  author = {Poldrack, Russell A. and Barch, Deanna M. and Mitchell, Jason P. and Wager, Tor D. and Wagner, Anthony D. and Devlin, Joseph T. and Cumba, Chad and Koyejo, Oluwasanmi and Milham, Michael P.},
  year = {2013},
  journal = {Frontiers in Neuroinformatics},
  volume = {7},
  issn = {1662-5196},
  doi = {10.3389/fninf.2013.00012},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FBH26NIE\\Poldrack et al. - 2013 - Toward open sharing of task-based fMRI data the O.pdf;C\:\\Users\\dirko\\Zotero\\storage\\JVNWJDWK\\Poldrack, Barch et al. 2013 - Toward open sharing of task-based.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UY92EXCY\\Poldrack et al. - 2013 - Toward open sharing of task-based fMRI data the O.pdf}
}

@article{poldrack_2014,
  title = {Making Big Data Open: Data Sharing in Neuroimaging},
  shorttitle = {Making Big Data Open},
  author = {Poldrack, Russell A and Gorgolewski, Krzysztof J},
  year = {2014},
  month = nov,
  journal = {Nature Neuroscience},
  volume = {17},
  number = {11},
  pages = {1510--1517},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.3818},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DMS3U9F5\\Poldrack and Gorgolewski - 2014 - Making big data open data sharing in neuroimaging.pdf}
}

@article{poldrack_2015,
  title = {The Publication and Reproducibility Challenges of Shared Data},
  author = {Poldrack, Russell A. and Poline, Jean-Baptiste},
  year = {2015},
  month = feb,
  journal = {Trends in Cognitive Sciences},
  volume = {19},
  number = {2},
  pages = {59--61},
  issn = {13646613},
  doi = {10.1016/j.tics.2014.11.008},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KFDXU33H\\Poldrack_TICS_15.pdf;C\:\\Users\\dirko\\Zotero\\storage\\KLZ9HVUK\\Poldrack and Poline - 2015 - The publication and reproducibility challenges of .pdf}
}

@article{poldrack_2016,
  title = {Scanning the {{Horizon}}: {{Towards}} Transparent and Reproducible Neuroimaging Research},
  shorttitle = {Scanning the {{Horizon}}},
  author = {Poldrack, Russell and Baker, Chris I and Durnez, Joke and Gorgolewski, Krzysztof and Matthews, Paul M and Munafo, Marcus and Nichols, Thomas and Poline, Jean-Baptiste and Vul, Edward and Yarkoni, Tal},
  year = {2016},
  month = oct,
  doi = {10.1101/059188},
  abstract = {Neuroimaging has transformed our ability to probe the neurobiological basis of behaviour. As neuroimaging technologies become more widely available, they are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions drawn from many human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, analytical flexibility, and lack of direct replication apply to many fields, but perhaps particularly to neuroimaging. In this O pinion article, we discuss these problems, outline current and suggested best practices to improve the quality and reproducibility of neuroimaging findings, and describe how we think the field should evolve if it is to produce the most meaningful answers to current and future research questions.},
  langid = {english},
  annotation = {00135},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\N68N4PFT\\Poldrack et al. - 2016 - Scanning the Horizon Towards transparent and repr.pdf}
}

@article{poldrack_2017,
  title = {Scanning the Horizon: Towards Transparent and Reproducible Neuroimaging Research},
  shorttitle = {Scanning the Horizon},
  author = {Poldrack, Russell A. and Baker, Chris I. and Durnez, Joke and Gorgolewski, Krzysztof J. and Matthews, Paul M. and Munaf{\`o}, Marcus R. and Nichols, Thomas E. and Poline, Jean-Baptiste and Vul, Edward and Yarkoni, Tal},
  year = {2017},
  month = feb,
  journal = {Nature Reviews Neuroscience},
  volume = {18},
  number = {2},
  pages = {115--126},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/nrn.2016.167},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EK4AG4UB\\Poldrack et al. - 2017 - Scanning the horizon towards transparent and repr.pdf}
}

@article{poldrack_2019,
  title = {Computational and {{Informatic Advances}} for {{Reproducible Data Analysis}} in {{Neuroimaging}}},
  author = {Poldrack, Russell A. and Gorgolewski, Krzysztof J. and Varoquaux, Ga{\"e}l},
  year = {2019},
  month = jul,
  journal = {Annual Review of Biomedical Data Science},
  volume = {2},
  number = {1},
  pages = {119--138},
  issn = {2574-3414, 2574-3414},
  doi = {10.1146/annurev-biodatasci-072018-021237},
  abstract = {The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.},
  langid = {english},
  annotation = {00010},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LXZ5IZGW\\Poldrack et al. - 2019 - Computational and Informatic Advances for Reproduc.pdf}
}

@article{poldrack_2019a,
  title = {The {{Costs}} of {{Reproducibility}}},
  author = {Poldrack, Russell A.},
  year = {2019},
  month = jan,
  journal = {Neuron},
  volume = {101},
  number = {1},
  pages = {11--14},
  issn = {08966273},
  doi = {10.1016/j.neuron.2018.11.030},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YYT3ZBWP\\Poldrack - 2019 - The Costs of Reproducibility.pdf}
}

@article{polich_2007,
  title = {Updating {{P300}}: {{An}} Integrative Theory of {{P3a}} and {{P3b}}},
  shorttitle = {Updating {{P300}}},
  author = {Polich, John},
  year = {2007},
  month = oct,
  journal = {Clinical Neurophysiology},
  volume = {118},
  number = {10},
  pages = {2128--2148},
  issn = {13882457},
  doi = {10.1016/j.clinph.2007.04.019},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ETLAJQPZ\\Polich_2007_Clinical Neurophysiology_Updating P300.pdf}
}

@article{poline_,
  title = {Statistical {{Inference}} and the {{Theory}} of {{Random Fields}}},
  author = {Poline, J-B and Holmes, A P and Worsley, K J and Friston, K J},
  pages = {24},
  langid = {english},
  annotation = {00011},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2MTELNAH\\Poline et al. - Statistical Inference and the Theory of Random Fie.pdf}
}

@article{poline_1997,
  title = {Combining Spatial Extent and Peak Intensity to Test for Activations in Functional Imaging},
  author = {Poline, Jean-Baptiste and Worsley, Keith J. and Evans, Alan C. and Friston, Karl J.},
  year = {1997},
  journal = {Neuroimage},
  volume = {5},
  number = {2},
  pages = {83--96},
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}

@article{poline_2012,
  title = {Data Sharing in Neuroimaging Research},
  author = {Poline, Jean-Baptiste and Breeze, Janis L. and Ghosh, Satrajit and Gorgolewski, Krzysztof and Halchenko, Yaroslav O. and Hanke, Michael and Haselgrove, Christian and Helmer, Karl G. and Keator, David B. and Marcus, Daniel S. and Poldrack, Russell A. and Schwartz, Yannick and Ashburner, John and Kennedy, David N.},
  year = {2012},
  journal = {Frontiers in Neuroinformatics},
  volume = {6},
  issn = {1662-5196},
  doi = {10.3389/fninf.2012.00009},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3AMNE5UR\\Poline et al. - 2012 - Data sharing in neuroimaging research.pdf;C\:\\Users\\dirko\\Zotero\\storage\\BZRENYPE\\Poline et al. - 2012 - Data sharing in neuroimaging research.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TADQZH89\\Poline, Breeze et al. 2012 - Data sharing in neuroimaging research.pdf}
}

@article{poline_2012a,
  title = {The General Linear Model and {{fMRI}}: Does Love Last Forever?},
  shorttitle = {The General Linear Model and {{fMRI}}},
  author = {Poline, Jean-Baptiste and Brett, Matthew},
  year = {2012},
  month = aug,
  journal = {NeuroImage},
  volume = {62},
  number = {2},
  pages = {871--880},
  issn = {1095-9572},
  doi = {10.1016/j.neuroimage.2012.01.133},
  abstract = {In this review, we first set out the general linear model (GLM) for the non technical reader, as a tool able to do both linear regression and ANOVA within the same flexible framework. We present a short history of its development in the fMRI community, and describe some interesting examples of its early use. We offer a few warnings, as the GLM relies on assumptions that may not hold in all situations. We conclude with a few wishes for the future of fMRI analyses, with or without the GLM. The appendix develops some aspects of use of contrasts for testing for the more technical reader.},
  langid = {english},
  pmid = {22343127},
  keywords = {Brain,Brain Mapping,History; 20th Century,History; 21st Century,Humans,Image Processing; Computer-Assisted,Linear Models,Magnetic Resonance Imaging},
  annotation = {00067},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3KWV6QWF\\Poline and Brett - 2012 - The general linear model and fMRI Does love last .pdf;C\:\\Users\\dirko\\Zotero\\storage\\XTC5LZLE\\Poline und Brett - 2012 - The general linear model and fMRI Does love last .pdf}
}

@book{popper_1935,
  title = {{Logik der Forschung}},
  author = {Popper, Karl},
  year = {1935},
  publisher = {{Springer Vienna}},
  address = {{Vienna}},
  doi = {10.1007/978-3-7091-4177-9},
  isbn = {978-3-7091-2021-7 978-3-7091-4177-9},
  langid = {german},
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}

@article{porcaro_2010,
  title = {Functional Source Separation Improves the Quality of Single Trial Visual Evoked Potentials Recorded during Concurrent {{EEG-fMRI}}},
  author = {Porcaro, Camillo and Ostwald, Dirk and Bagshaw, Andrew P.},
  year = {2010},
  month = mar,
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  volume = {50},
  number = {1},
  pages = {112--123},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.12.002},
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}

@article{porcaro_2011,
  title = {The Relationship between the Visual Evoked Potential and the Gamma Band Investigated by Blind and Semi-Blind Methods},
  author = {Porcaro, Camillo and Ostwald, Dirk and Hadjipapas, Avgis and Barnes, Gareth R. and Bagshaw, Andrew P.},
  year = {2011},
  month = jun,
  journal = {NeuroImage},
  volume = {56},
  number = {3},
  pages = {1059--1071},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2011.03.008},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RZ7G92KK\\Porcaro et al. - 2011 - The relationship between the visual evoked potenti.pdf}
}

@article{porst_2000,
  title = {{Question Wording \textendash{} Zur Formulierung von Fragebogen-Fragen}},
  author = {Porst, Rolf},
  year = {2000},
  journal = {GESIS - Leibniz-Institut f\"ur Sozialwissenschaften},
  pages = {12},
  abstract = {The article is concerned with different rules of question wording in social science questionnaires. All these rules refer to basic aspects of question wording and claim validity to all modes of data collection (face to face surveys, telephone surveys, and mail survey).},
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  file = {C\:\\Users\\dirko\\Zotero\\storage\\FUE9Q8UT\\Porst - Question Wording – Zur Formulierung von Fragebogen.pdf}
}

@article{pounds_2004,
  title = {Improving False Discovery Rate Estimation},
  author = {Pounds, S. and Cheng, C.},
  year = {2004},
  month = jul,
  journal = {Bioinformatics},
  volume = {20},
  number = {11},
  pages = {1737--1745},
  issn = {1367-4803, 1460-2059},
  doi = {10.1093/bioinformatics/bth160},
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@article{poupon_2017,
  title = {The {{Tanenbaum Open Science Institute}}: {{Leading}} a {{Paradigm Shift}} at the {{Montreal Neurological Institute}}},
  shorttitle = {The {{Tanenbaum Open Science Institute}}},
  author = {Poupon, Viviane and Seyller, Annabel and Rouleau, Guy A.},
  year = {2017},
  month = aug,
  journal = {Neuron},
  volume = {95},
  number = {5},
  pages = {1002--1006},
  issn = {0896-6273},
  doi = {10.1016/j.neuron.2017.07.026},
  langid = {english},
  pmid = {28858611},
  keywords = {Neuroscience,Open Access,Open Biobank,Open Data,Open Drug Discovery,Open IP,Open Science,Open Science Impact,Research Culture Change},
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}

@article{powell_,
  title = {Generating {{Realisations}} of {{Stationary Gaussian Random Fields}} by {{Circulant Embedding}}.},
  author = {Powell, Catherine E},
  pages = {6},
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}

@book{powell_2012,
  title = {Approximate {{Dynamic Programming}}},
  author = {Powell, Warren B},
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@book{press_2007,
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@article{press_2020,
  title = {Learning to {{Perceive}} and {{Perceiving}} to {{Learn}}},
  author = {Press, Clare and Kok, Peter and Yon, Daniel},
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  journal = {Trends in Cognitive Sciences},
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@article{press_2020a,
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  author = {Press, Clare and Kok, Peter and Yon, Daniel},
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  journal = {Trends in Cognitive Sciences},
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  issn = {13646613},
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@article{prufer_2002,
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@book{puntanen_2011a,
  title = {Matrix Tricks for Linear Statistical Models: Our Personal Top Twenty},
  shorttitle = {Matrix Tricks for Linear Statistical Models},
  author = {Puntanen, Simo and Styan, George P. H. and Isotalo, Jarkko},
  year = {2011},
  publisher = {{Springer}},
  address = {{Heidelberg Dordrecht London New York}},
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@book{puterman_2005,
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@article{qin_2010,
  title = {A Comparative Study of Different References for {{EEG}} Default Mode Network: {{The}} Use of the Infinity Reference},
  shorttitle = {A Comparative Study of Different References for {{EEG}} Default Mode Network},
  author = {Qin, Yun and Xu, Peng and Yao, Dezhong},
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  journal = {Clinical Neurophysiology},
  volume = {121},
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  pages = {1981--1991},
  issn = {13882457},
  doi = {10.1016/j.clinph.2010.03.056},
  abstract = {Objective: The choice of electroencephalograph (EEG) reference is a critical issue for the study of brain activity. The present study addressed the use of the infinity reference obtained by the reference electrode standardisation technique (REST) in the study of EEG default mode network (DMN). Methods: A total of 100 randomly positioned source configurations, each consisting of two dipoles with coherent waveforms, were adopted for simulating EEG networks. Dense (129-channel), eyes-closed EEG was recorded from 15 subjects. Simulated data with infinity as reference and the real data were re-referenced to reconstructed infinity (REST), their average (AR), linked mastoids (LM) and left mastoid (L) references. For simulated data, the effects of different references on coherence and network were investigated. For real data, spectral properties of seven conventional EEG frequency bands were first analysed and then DMN was constructed based on the coherence. Results: The simulation showed that REST can exactly recover the true EEG network configuration. For real EEG data, significant differences among references were found for the power spectra, coherence and DMN configuration. Compared with REST, the long-distance connectivity between anterior and posterior areas was strengthened by AR, and the connectivity over posterior areas was destroyed when LM and L were employed. Moreover, all comparisons demonstrated frequency-dependent reference effects. Conclusions: Non-neutral reference influences the power spectra, coherence as well as the network analysis. REST demonstrates its validity in data referencing, and meanwhile, AR is much closer to REST than the other references in terms of spectra and coherence. However, the DMN alters a great deal with AR. Significance: The results underscore the importance of considering EEG reference effects in the functional connectivity studies. REST is a promising reference technique for objective comparison in cross-laboratory studies and clinical practices.},
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@article{quiroga_2003,
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  series = {Computational Neuroscience},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-01764-0},
  langid = {english},
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@article{rafaeli_2006,
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  author = {Rafaeli, Eshkol and Revelle, William},
  year = {2006},
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  journal = {Motivation and Emotion},
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  number = {1},
  pages = {1--12},
  issn = {0146-7239, 1573-6644},
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  abstract = {Within the debate on the structure of affect, a consensus began emerging in the last decade regarding the bipolarity of happiness\textendash sadness. We argue that this consensus is premature. Focusing on the psychometrics of momentary affect, particularly happiness and sadness, and using a simulation study, a large-scale data set, and 2 experiments manipulating affect, we plot a map of affective space that departs from the consensus. One key departure is the finding that happiness and sadness are not bipolar opposites. Another is that nonuniform skewness plays a major role in studies of affective structure, but can be addressed with appropriate analyses.},
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}

@article{rafaeli_2006a,
  title = {A Premature Consensus: Are Happiness and Sadness Truly Opposite Affects?},
  shorttitle = {A Premature Consensus},
  author = {Rafaeli, Eshkol and Revelle, William},
  year = {2006},
  month = mar,
  journal = {Motivation and Emotion},
  volume = {30},
  number = {1},
  pages = {1--12},
  issn = {0146-7239, 1573-6644},
  doi = {10.1007/s11031-006-9004-2},
  abstract = {Within the debate on the structure of affect, a consensus began emerging in the last decade regarding the bipolarity of happiness\textendash sadness. We argue that this consensus is premature. Focusing on the psychometrics of momentary affect, particularly happiness and sadness, and using a simulation study, a large-scale data set, and 2 experiments manipulating affect, we plot a map of affective space that departs from the consensus. One key departure is the finding that happiness and sadness are not bipolar opposites. Another is that nonuniform skewness plays a major role in studies of affective structure, but can be addressed with appropriate analyses.},
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}

@article{raiffa_,
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@techreport{ramakrishnan_2019,
  type = {Preprint},
  title = {A Machine Learning Approach to Identifying Objective Biomarkers of Anxiety and Stress},
  author = {Ramakrishnan, Arjun and Pardes, Adam and Lynch, William and Molaro, Christopher and Platt, Michael Louis},
  year = {2019},
  month = aug,
  institution = {{Neuroscience}},
  doi = {10.1101/745315},
  abstract = {Anxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state. Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety \textendash{} a vulnerability foreshadowing development of anxiety and mood disorders \textendash{} remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to nonstressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with \textasciitilde 96\% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with \textasciitilde 84\% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.},
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}

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@article{ramstead_2019,
  title = {A Tale of Two Densities: Active Inference Is Enactive Inference},
  shorttitle = {A Tale of Two Densities},
  author = {Ramstead, Maxwell JD and Kirchhoff, Michael D and Friston, Karl J},
  year = {2019},
  month = jul,
  journal = {Adaptive Behavior},
  pages = {105971231986277},
  issn = {1059-7123, 1741-2633},
  doi = {10.1177/1059712319862774},
  abstract = {The aim of this article is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) \textendash{} and its corollary, active inference \textendash{} in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature, because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain \textendash{} variously known as predictive processing, predictive coding or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference \textendash what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control \textendash{} the self-organising, belief-guided selection of action policies \textendash{} and do not have the properties ascribed by structural representationalists.},
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@article{ranganath_,
  ids = {ranganath_black_nodate-1},
  title = {Black {{Box Variational Inference}}},
  author = {Ranganath, Rajesh and Gerrish, Sean and Blei, David M},
  pages = {9},
  abstract = {Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires significant model-specific analysis. These efforts can hinder and deter us from quickly developing and exploring a variety of models for a problem at hand. In this paper, we present a ``black box'' variational inference algorithm, one that can be quickly applied to many models with little additional derivation. Our method is based on a stochastic optimization of the variational objective where the noisy gradient is computed from Monte Carlo samples from the variational distribution. We develop a number of methods to reduce the variance of the gradient, always maintaining the criterion that we want to avoid difficult model-based derivations. We evaluate our method against the corresponding black box sampling based methods. We find that our method reaches better predictive likelihoods much faster than sampling methods. Finally, we demonstrate that Black Box Variational Inference lets us easily explore a wide space of models by quickly constructing and evaluating several models of longitudinal healthcare data.},
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}

@article{ranganath_2015,
  title = {Hierarchical {{Variational Models}}},
  author = {Ranganath, Rajesh and Tran, Dustin and Blei, David M.},
  year = {2015},
  month = nov,
  journal = {arXiv:1511.02386 [cs, stat]},
  eprint = {1511.02386},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However, a central question remains: How to specify an expressive variational distribution that maintains efficient computation? To address this, we develop hierarchical variational models (HVMs). HVMs augment a variational approximation with a prior on its parameters, which allows it to capture complex structure for both discrete and continuous latent variables. The algorithm we develop is black box, can be used for any HVM, and has the same computational efficiency as the original approximation. We study HVMs on a variety of deep discrete latent variable models. HVMs generalize other expressive variational distributions and maintains higher fidelity to the posterior.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Learning,Statistics - Computation,Statistics - Machine Learning,Statistics - Methodology},
  annotation = {00073},
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}

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}

@article{rao_1972,
  title = {Recent {{Trends}} of {{Research Work}} in {{Multivariate Analysis}}},
  author = {Rao, C Radhakrishna},
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@book{rao_2002,
  title = {Linear Statistical Inference and Its Applications},
  author = {Rao, Calyampudi Radhakrishna},
  year = {2002},
  series = {Wiley Series in Probability and Statistics},
  edition = {2. ed., paperback ed},
  publisher = {{Wiley}},
  address = {{New York}},
  isbn = {978-0-471-21875-3},
  langid = {english},
  annotation = {OCLC: 248377203},
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}

@article{rao_2010,
  title = {Decision {{Making Under Uncertainty}}: {{A Neural Model Based}} on {{Partially Observable Markov Decision Processes}}},
  shorttitle = {Decision {{Making Under Uncertainty}}},
  author = {Rao, Rajesh P. N.},
  year = {2010},
  journal = {Frontiers in Computational Neuroscience},
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@article{rao_2016,
  title = {Testing {{Point Null Hypothesis}} of a {{Normal Mean}} and the {{Truth}}: 21st {{Century Perspective}}},
  shorttitle = {Testing {{Point Null Hypothesis}} of a {{Normal Mean}} and the {{Truth}}},
  author = {Rao, Calyampudi Radhakrishna and Lovric, Miodrag M.},
  year = {2016},
  month = nov,
  journal = {Journal of Modern Applied Statistical Methods},
  volume = {15},
  number = {2},
  pages = {2--21},
  issn = {1538-9472},
  doi = {10.22237/jmasm/1478001660},
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@book{raoult_1980,
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  editor = {Raoult, Jean Pierre},
  year = {1980},
  series = {({{Lecture}} Notes in Mathematics},
  number = {821)},
  publisher = {{Springer}},
  address = {{Berlin, Heidelberg [usw.]}},
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@book{rasmussen_2006,
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  author = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
  year = {2006},
  series = {Adaptive Computation and Machine Learning},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-18253-9},
  langid = {english},
  lccn = {QA274.4 .R37 2006},
  keywords = {Data processing,Gaussian processes,Machine learning,Mathematical models},
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@article{ratcliff_,
  title = {A {{Theory}} of {{Memory Retrieval}}},
  author = {Ratcliff, Roger},
  pages = {50},
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@misc{ratfurinformationsinfrastrukturen_2017,
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  author = {{Rat f{\"u}r Informationsinfrastrukturen}},
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@book{ravichandiran_2018,
  title = {Hands-on Reinforcement Learning with {{Python}}: Master Reinforcement and Deep Reinforcement Learning Using {{OpenAI Gym}} and {{TensorFlow}}},
  shorttitle = {Hands-on Reinforcement Learning with {{Python}}},
  author = {Ravichandiran, Sudharsan},
  year = {2018},
  isbn = {978-1-78883-691-3},
  langid = {english},
  annotation = {00000  OCLC: 1047731019},
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@manual{rcoreteam_2021,
  type = {Manual},
  title = {R: {{A}} Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  year = {2021},
  address = {{Vienna, Austria}},
  organization = {{R Foundation for Statistical Computing}}
}

@book{reemtsen_2000,
  title = {{Einf\"uhrung in die nichtlineare Optimierung}},
  author = {Reemtsen, Rembert},
  year = {2000},
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@book{reiss_2012,
  title = {{Experimentelle Psychologie: von der Theorie zur Praxis}},
  shorttitle = {{Experimentelle Psychologie}},
  author = {Rei{\ss}, Siegbert and Sarris, Viktor},
  year = {2012},
  series = {{Pearson Studium Psychologie}},
  publisher = {{Pearson}},
  address = {{M\"unchen}},
  abstract = {Die beiden Hochschullehrer machen mit den wissenschaftstheoretischen Grundlagen des psychologischen Experiments vertraut und f\"uhren in die Methodik der Experimentalpsychologie ein. Ein mit zahlreichen experimentalpsychologischen Beispielen, inhaltlichen Vertiefungen in Exkursen, Hervorhebungen wichtiger Aspekte in K\"asten und anderem mehr didaktisch sehr gut aufbereitetes Lehrbuch, jetzt grundlegend \"uberarbeitet (die - hier nicht angezeigte - 1. Auflage erschien 2005 unter Viktor Sarris: "Kurzer Leitfaden der Experimentalpsychologie"). F\"ur Psychologie-Studenten; noch vor der Einf\"uhrung von Oswald Huber (ID-G 11/98, 5. \"uberarbeitete Auflage 2009). (2)},
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}

@article{reiss_2015,
  title = {Cross-Validation and Hypothesis Testing in Neuroimaging: {{An}} Irenic Comment on the Exchange between {{Friston}} and {{Lindquist}} et Al.},
  shorttitle = {Cross-Validation and Hypothesis Testing in Neuroimaging},
  author = {Reiss, Philip T.},
  year = {2015},
  month = aug,
  journal = {NeuroImage},
  volume = {116},
  pages = {248--254},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2015.04.032},
  abstract = {The ``ten ironic rules for statistical reviewers'' presented by Friston (2012) prompted a rebuttal by Lindquist et al. (2013), which was followed by a rejoinder by Friston (2013). A key issue left unresolved in this discussion is the use of cross-validation to test the significance of predictive analyses. This note discusses the role that crossvalidation-based and related hypothesis tests have come to play in modern data analyses, in neuroimaging and other fields. It is shown that such tests need not be suboptimal and can fill otherwise-unmet inferential needs.},
  langid = {english},
  annotation = {00004},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZESTPHHM\\Reiss - 2015 - Cross-validation and hypothesis testing in neuroim.pdf}
}

@book{rencher_2002,
  title = {Methods of Multivariate Analysis},
  author = {Rencher, Alvin C.},
  year = {2002},
  series = {Wiley Series in Probability and Mathematical Statistics},
  edition = {2nd ed},
  publisher = {{J. Wiley}},
  address = {{New York}},
  isbn = {978-0-471-41889-4},
  langid = {english},
  lccn = {QA278 .R45 2002},
  keywords = {Multivariate analysis},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\G63IRKD3\\Rencher - 2002 - Methods of multivariate analysis.pdf}
}

@book{rencher_2008,
  title = {Linear Models in Statistics},
  author = {Rencher, Alvin C. and Schaalje, G. Bruce},
  year = {2008},
  edition = {2nd ed},
  publisher = {{Wiley-Interscience}},
  address = {{Hoboken, N.J}},
  isbn = {978-0-471-75498-5},
  langid = {english},
  lccn = {QA276 .R425 2008},
  keywords = {Linear models (Statistics)},
  annotation = {OCLC: ocn144331522},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\P7ZVFGUP\\Rencher und Schaalje - 2008 - Linear models in statistics.pdf}
}

@incollection{rescorla_1972,
  title = {A {{Theory}} of {{Pavlovian Conditioning}}: {{Variations}} in the {{Effectiveness}} of {{Reinforcement}} and {{Nonreinforcement}}},
  booktitle = {Classical {{Conditioning II}}},
  author = {Rescorla, Robert A and Wagner, Allan R},
  year = {1972},
  pages = {18},
  address = {{New York}},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TQ8GBHAY\\Rescorla und Wagner - 1972 - A Theory of Pavlovian Conditioning Variations in .pdf}
}

@article{rezende_2014,
  title = {Stochastic {{Backpropagation}} and {{Approximate Inference}} in {{Deep Generative Models}}},
  author = {Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan},
  year = {2014},
  month = jan,
  journal = {arXiv:1401.4082 [cs, stat]},
  eprint = {1401.4082},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent an approximate posterior distribution and uses this for optimisation of a variational lower bound. We develop stochastic backpropagation \textendash{} rules for gradient backpropagation through stochastic variables \textendash{} and derive an algorithm that allows for joint optimisation of the parameters of both the generative and recognition models. We demonstrate on several real-world data sets that by using stochastic backpropagation and variational inference, we obtain models that are able to generate realistic samples of data, allow for accurate imputations of missing data, and provide a useful tool for high-dimensional data visualisation.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Statistics - Computation,Statistics - Machine Learning,Statistics - Methodology},
  annotation = {01452},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SJZKLZ3F\\Rezende et al. - 2014 - Stochastic Backpropagation and Approximate Inferen.pdf}
}

@article{rezende_2020,
  title = {Causally {{Correct Partial Models}} for {{Reinforcement Learning}}},
  author = {Rezende, Danilo J. and Danihelka, Ivo and Papamakarios, George and Ke, Nan Rosemary and Jiang, Ray and Weber, Theophane and Gregor, Karol and Merzic, Hamza and Viola, Fabio and Wang, Jane and Mitrovic, Jovana and Besse, Frederic and Antonoglou, Ioannis and Buesing, Lars},
  year = {2020},
  month = feb,
  journal = {arXiv:2002.02836 [cs, stat]},
  eprint = {2002.02836},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images). For this reason, previous works have considered partial models, which model only part of the observation. In this paper, we show that partial models can be causally incorrect: they are confounded by the observations they don't model, and can therefore lead to incorrect planning. To address this, we introduce a general family of partial models that are provably causally correct, yet remain fast because they do not need to fully model future observations.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\I9XQSM77\\Rezende et al. - 2020 - Causally Correct Partial Models for Reinforcement .pdf}
}

@article{rich_2020,
  title = {{{AI}} Reflections in 2019},
  author = {Rich, Alexander S. and Rudin, Cynthia and Jacoby, David M. P. and Freeman, Robin and Wearn, Oliver R. and Shevlin, Henry and Dihal, Kanta and {\'O}h{\'E}igeartaigh, Se{\'a}n S. and Butcher, James and Lippi, Marco and Palka, Przemyslaw and Torroni, Paolo and Wongvibulsin, Shannon and Begoli, Edmon and Schneider, Gisbert and Cave, Stephen and Sloane, Mona and Moss, Emmanuel and Rahwan, Iyad and Goldberg, Ken and Howard, David and Floridi, Luciano and Stilgoe, Jack},
  year = {2020},
  month = jan,
  journal = {Nature Machine Intelligence},
  pages = {s42256-019-0141-1},
  issn = {2522-5839},
  doi = {10.1038/s42256-019-0141-1},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SZ77DPSH\\Rich et al. - 2020 - AI reflections in 2019.pdf}
}

@article{richardbellman_1960,
  title = {A {{Problem}} in the {{Sequential Design}} of {{Experiments}}},
  author = {Richard Bellman},
  year = {1960},
  journal = {Sankhy\=a: The Indian Journal of Statistics (1933-1960)},
  volume = {16},
  number = {3/4},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2XJ78J22\\Richard Bellman - 1960 - A Problem in the Sequential Design of Experiments.pdf}
}

@article{richards_2019,
  ids = {richards_deep_2019-1},
  title = {A Deep Learning Framework for Neuroscience},
  author = {Richards, Blake A. and Lillicrap, Timothy P. and Beaudoin, Philippe and Bengio, Yoshua and Bogacz, Rafal and Christensen, Amelia and Clopath, Claudia and Costa, Rui Ponte and {de Berker}, Archy and Ganguli, Surya and Gillon, Colleen J. and Hafner, Danijar and Kepecs, Adam and Kriegeskorte, Nikolaus and Latham, Peter and Lindsay, Grace W. and Miller, Kenneth D. and Naud, Richard and Pack, Christopher C. and Poirazi, Panayiota and Roelfsema, Pieter and Sacramento, Jo{\~a}o and Saxe, Andrew and Scellier, Benjamin and Schapiro, Anna C. and Senn, Walter and Wayne, Greg and Yamins, Daniel and Zenke, Friedemann and Zylberberg, Joel and Therien, Denis and Kording, Konrad P.},
  year = {2019},
  month = nov,
  journal = {Nature Neuroscience},
  volume = {22},
  number = {11},
  pages = {1761--1770},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-019-0520-2},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6TYPE7I7\\Richards et al. - 2019 - A deep learning framework for neuroscience.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UFD4DKKK\\Richards et al. - 2019 - A deep learning framework for neuroscience.pdf}
}

@book{richter_2019,
  title = {{Statistisches und maschinelles Lernen: G\"angige Verfahren im \"Uberblick}},
  shorttitle = {{Statistisches und maschinelles Lernen}},
  author = {Richter, Stefan},
  year = {2019},
  publisher = {{Springer Berlin Heidelberg}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-662-59354-7},
  isbn = {978-3-662-59353-0 978-3-662-59354-7},
  langid = {german},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TFEGQKE8\\Richter - 2019 - Statistisches und maschinelles Lernen Gängige Ver.pdf}
}

@article{rigoux_2014,
  title = {Bayesian Model Selection for Group Studies \textemdash{} {{Revisited}}},
  author = {Rigoux, L. and Stephan, K. E. and Friston, K. J. and Daunizeau, J.},
  year = {2014},
  month = jan,
  journal = {NeuroImage},
  volume = {84},
  pages = {971--985},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2013.08.065},
  abstract = {In this paper, we revisit the problem of Bayesian model selection (BMS) at the group level. We originally addressed this issue in Stephan et al. (2009), where models are treated as random effects that could differ between subjects, with an unknown population distribution. Here, we extend this work, by (i) introducing the Bayesian omnibus risk (BOR) as a measure of the statistical risk incurred when performing group BMS, (ii) highlighting the difference between random effects BMS and classical random effects analyses of parameter estimates, and (iii) addressing the problem of between group or condition model comparisons. We address the first issue by quantifying the chance likelihood of apparent differences in model frequencies. This leads to the notion of protected exceedance probabilities. The second issue arises when people want to ask ``whether a model parameter is zero or not'' at the group level. Here, we provide guidance as to whether to use a classical second-level analysis of parameter estimates, or random effects BMS. The third issue rests on the evidence for a difference in model labels or frequencies across groups or conditions. Overall, we hope that the material presented in this paper finesses the problems of group-level BMS in the analysis of neuroimaging and behavioural data.},
  keywords = {Between-condition comparison,Between-group comparison,DCM,Exceedance probability,Mixed effects,Random effects,Statistical risk},
  annotation = {00120},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8K8Y8L2F\\Rigoux et al_2014_NeuroImage_Bayesian model selection for group studies — Revisited.pdf;C\:\\Users\\dirko\\Zotero\\storage\\VJLPXQFS\\S1053811913009300.html}
}

@article{ringach_2002,
  title = {Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque Primary Visual Cortex},
  author = {Ringach, Dario L.},
  year = {2002},
  journal = {Journal of neurophysiology},
  volume = {88},
  number = {1},
  pages = {455--463},
  annotation = {00338},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4S4DFMMT\\Ringach_2002_Journal of Neurophysiology_Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque.pdf}
}

@book{ritchie_2020,
  title = {Science {{Fictions}} - {{Exposing Fraud}}, {{Bias}}, {{Negligence}} and {{Hype}} in {{Science}}},
  author = {Ritchie, Stuart},
  year = {2020},
  publisher = {{Penguin}},
  isbn = {978-1-84792-565-7},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VEZ44SBG\\Ritchie - 2020 - Science Fictions - Exposing Fraud, Bias, Negligenc.pdf}
}

@book{robert_2007,
  title = {The {{Bayesian}} Choice: From Decision-Theoretic Foundations to Computational Implementation},
  shorttitle = {The {{Bayesian}} Choice},
  author = {Robert, Christian P.},
  year = {2007},
  series = {Springer Texts in Statistics},
  edition = {2nd ed},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-71598-8},
  langid = {english},
  lccn = {QA279.5 .R6313 2007},
  keywords = {Bayesian statistical decision theory},
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}

@article{robert_2009,
  title = {Harold {{Jeffreys}}'s {{Theory}} of {{Probability Revisited}}},
  author = {Robert, Christian P. and Chopin, Nicolas and Rousseau, Judith},
  year = {2009},
  month = may,
  journal = {Statistical Science},
  volume = {24},
  number = {2},
  eprint = {0804.3173},
  eprinttype = {arxiv},
  pages = {141--172},
  issn = {0883-4237},
  doi = {10.1214/09-STS284},
  abstract = {Published exactly seventy years ago, Jeffreys's Theory of Probability (1939) has had a unique impact on the Bayesian community and is now considered to be one of the main classics in Bayesian Statistics as well as the initiator of the objective Bayes school. In particular, its advances on the derivation of noninformative priors as well as on the scaling of Bayes factors have had a lasting impact on the field. However, the book reflects the characteristics of the time, especially in terms of mathematical rigor. In this paper we point out the fundamental aspects of this reference work, especially the thorough coverage of testing problems and the construction of both estimation and testing noninformative priors based on functional divergences. Our major aim here is to help modern readers in navigating in this difficult text and in concentrating on passages that are still relevant today.},
  archiveprefix = {arXiv},
  keywords = {Mathematics - History and Overview,Mathematics - Statistics Theory},
  annotation = {00079},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RW6W5N6E\\Robert et al_2009_Statistical Science_Harold Jeffreys's Theory of Probability Revisited.pdf;C\:\\Users\\dirko\\Zotero\\storage\\3TF5EZIP\\0804.html}
}

@article{rodrigues_2009,
  title = {Transitions to Spike-Wave Oscillations and Epileptic Dynamics in a Human Cortico-Thalamic Mean-Field Model},
  author = {Rodrigues, Serafim and Barton, David and Szalai, Robert and Benjamin, Oscar and Richardson, Mark P. and Terry, John R.},
  year = {2009},
  month = dec,
  journal = {Journal of Computational Neuroscience},
  volume = {27},
  number = {3},
  pages = {507--526},
  issn = {0929-5313, 1573-6873},
  doi = {10.1007/s10827-009-0166-2},
  langid = {english},
  annotation = {00000},
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}

@unpublished{rodriguez_2007,
  title = {Lecture {{Notes}} on {{Generalized Linear Models}}},
  author = {Rodr{\'i}guez, G},
  year = {2007},
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}

@article{roiser_2016,
  ids = {roiser_minimum_2016},
  title = {Minimum Statistical Standards for Submissions to {{Neuroimage}}: {{Clinical}}},
  shorttitle = {Minimum Statistical Standards for Submissions to {{Neuroimage}}},
  author = {Roiser, J.P. and Linden, D.E. and {Gorno-Tempini}, M.L. and Moran, R.J. and Dickerson, B.C. and Grafton, S.T.},
  year = {2016},
  month = feb,
  journal = {NeuroImage: Clinical},
  volume = {12},
  pages = {1045--1047},
  issn = {22131582},
  doi = {10.1016/j.nicl.2016.08.002},
  langid = {english},
  annotation = {00014},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ABDW7R4D\\Roiser et al. - 2016 - Minimum statistical standards for submissions to N.pdf;C\:\\Users\\dirko\\Zotero\\storage\\NU73IH6G\\Roiser et al. - 2016 - Minimum statistical standards for submissions to N.pdf}
}

@book{rojas_1996,
  title = {Neural {{Networks}}},
  author = {Rojas, Raul},
  year = {1996},
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}

@article{rollings_2016,
  title = {Early Haemodynamic Changes Observed in Patients with Epilepsy, in a Visual Experiment and in Simulations},
  author = {Rollings, David T. and Assecondi, Sara and Ostwald, Dirk and Porcaro, Camillo and McCorry, Dougall and Bagary, Manny and Soryal, Imad and Bagshaw, Andrew P.},
  year = {2016},
  month = jan,
  journal = {Clinical Neurophysiology},
  volume = {127},
  number = {1},
  pages = {245--253},
  issn = {13882457},
  doi = {10.1016/j.clinph.2015.07.008},
  abstract = {Objective: The objective of this study was to investigate whether previously reported early blood oxygen level dependent (BOLD) changes in epilepsy could occur as a result of the modelling techniques rather than physiological changes. Methods: EEG-fMRI data were analysed from seven patients with focal epilepsy, six control subjects undergoing a visual experiment, in addition to simulations. In six separate analyses the event timing was shifted by either \`A9,\`A6,\`A3,+3,+6 or +9 s relative to the onset of the interictal epileptiform discharge (IED) or stimulus. Results: The visual dataset and simulations demonstrated an overlap between modelled haemodynamic response function (HRF) at event onset and at {$\pm$}3 s relative to onset, which diminished at {$\pm$}6 s. Pre-spike analysis at \`A6 s improved concordance with the assumed IED generating lobe relative to the standard HRF in 43\% of patients. Conclusion: The visual and simulated dataset findings indicate a form of ``temporal bleeding'', an overlap between the modelled HRF at time 0 and at {$\pm$}3 s which attenuated at {$\pm$}6 s. Pre-spike analysis at \`A6 s may improve concordance.},
  langid = {english},
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}

@article{rosa_2012,
  title = {Post-Hoc Selection of Dynamic Causal Models},
  author = {Rosa, M.J. and Friston, K. and Penny, W.},
  year = {2012},
  month = jun,
  journal = {Journal of Neuroscience Methods},
  volume = {208},
  number = {1},
  pages = {66--78},
  issn = {01650270},
  doi = {10.1016/j.jneumeth.2012.04.013},
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}

@article{rosenbaum_1983,
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  author = {Rosenbaum, Paul R and Rubin, Donald B},
  year = {1983},
  pages = {15},
  abstract = {The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a twodimensional plot.},
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}

@article{rosenblatt_,
  title = {Remarks on {{Some Nonparametric Estimates}} of a {{Density Function}}},
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}

@article{rosenblatt_1958,
  title = {The Perceptron: {{A}} Probabilistic Model for Information Storage and Organization in the Brain.},
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  author = {Rosenblatt, F.},
  year = {1958},
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  issn = {1939-1471, 0033-295X},
  doi = {10.1037/h0042519},
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}

@article{rosenkranz_2010,
  title = {Present and Future of Simultaneous {{EEG-fMRI}}},
  author = {Rosenkranz, Karin and Lemieux, Louis},
  year = {2010},
  month = dec,
  journal = {Magnetic Resonance Materials in Physics, Biology and Medicine},
  volume = {23},
  number = {5-6},
  pages = {309--316},
  issn = {0968-5243, 1352-8661},
  doi = {10.1007/s10334-009-0196-9},
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}

@book{rosenthal_1991,
  ids = {rosenthal_meta-analytic_1991-1},
  title = {Meta-Analytic Procedures for Social Research},
  author = {Rosenthal, Robert},
  year = {1991},
  series = {Applied Social Research Methods Series},
  edition = {Rev. ed},
  number = {v. 6},
  publisher = {{Sage Publications}},
  address = {{Newbury Park}},
  isbn = {978-0-8039-4245-5 978-0-8039-4246-2},
  langid = {english},
  lccn = {H62 .R6565 1991},
  keywords = {Research,Social sciences},
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@book{rosenthal_2006,
  ids = {rosenthal_first_2006a},
  title = {A First Look at Rigorous Probability Theory},
  author = {Rosenthal, Jeffrey S.},
  year = {2006},
  edition = {2nd ed},
  publisher = {{World Scientific}},
  address = {{Singapore ; Hackensack, N.J}},
  isbn = {978-981-270-371-2 978-981-270-370-5},
  langid = {english},
  lccn = {QA273 .R7835 2006},
  keywords = {Measure theory,Probabilities,Probability measures},
  annotation = {00273  OCLC: ocm81144993},
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}

@book{ross_2007,
  title = {Introduction to Probability Models},
  author = {Ross, Sheldon M.},
  year = {2007},
  edition = {9th ed},
  publisher = {{Academic Press}},
  address = {{Amsterdam ; Boston}},
  isbn = {978-0-12-598062-3 978-0-12-373635-2},
  langid = {english},
  lccn = {QA273 .R84 2007},
  keywords = {Probabilities},
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}

@article{rosseel_2012,
  title = {Lavaan: {{An R Package}} for {{Structural Equation Modeling}}},
  shorttitle = {{\textbf{Lavaan}}},
  author = {Rosseel, Yves},
  year = {2012},
  journal = {Journal of Statistical Software},
  volume = {48},
  number = {2},
  issn = {1548-7660},
  doi = {10.18637/jss.v048.i02},
  abstract = {Structural equation modeling (SEM) is a vast field and widely used by many applied researchers in the social and behavioral sciences. Over the years, many software packages for structural equation modeling have been developed, both free and commercial. However, perhaps the best state-of-the-art software packages in this field are still closedsource and/or commercial. The R package lavaan has been developed to provide applied researchers, teachers, and statisticians, a free, fully open-source, but commercial-quality package for latent variable modeling. This paper explains the aims behind the development of the package, gives an overview of its most important features, and provides some examples to illustrate how lavaan works in practice.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RGC9HRXN\\Rosseel - 2012 - lavaan  An R Package for Structural.pdf}
}

@article{rothman_1990,
  title = {No Adjustments Are Needed for Multiple Comparisons},
  author = {Rothman, Kenneth J.},
  year = {1990},
  journal = {Epidemiology},
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  annotation = {03117},
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}

@article{rousselet_2012,
  title = {Does {{Filtering Preclude Us}} from {{Studying ERP Time-Courses}}?},
  author = {Rousselet, Guillaume A.},
  year = {2012},
  journal = {Frontiers in Psychology},
  volume = {3},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2012.00131},
  langid = {english},
  annotation = {00058},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PZMG4FUG\\Rousselet - 2012 - Does Filtering Preclude Us from Studying ERP Time-.pdf}
}

@article{roweis_1998,
  title = {{{EM Algorithms}} for {{PCA}} and {{SPCA}}},
  author = {Roweis, Sam},
  year = {1998},
  pages = {7},
  abstract = {I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates missing information. I also introduce a new variant of PCA called sensible principal component analysis (SPCA) which defines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm. I report results on synthetic and real data showing that these EM algorithms correctly and efficiently find the leading eigenvectors of the covariance of datasets in a few iterations using up to hundreds of thousands of datapoints in thousands of dimensions.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BJ9X5GLI\\Roweis - EM Algorithms for PCA and SPCA.pdf}
}

@article{roweis_1999,
  title = {A {{Unifying Review}} of {{Linear Gaussian Models}}},
  author = {Roweis, Sam and Ghahramani, Zoubin},
  year = {1999},
  month = feb,
  journal = {Neural Computation},
  volume = {11},
  number = {2},
  pages = {305--345},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/089976699300016674},
  langid = {english},
  annotation = {00983},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\6F5I45J8\\Roweis and Ghahramani - 1999 - A Unifying Review of Linear Gaussian Models.pdf}
}

@article{roxin_2019,
  title = {Drift\textendash Diffusion Models for Multiple-Alternative Forced-Choice Decision Making},
  author = {Roxin, Alex},
  year = {2019},
  month = dec,
  journal = {The Journal of Mathematical Neuroscience},
  volume = {9},
  number = {1},
  pages = {5},
  issn = {2190-8567},
  doi = {10.1186/s13408-019-0073-4},
  abstract = {The canonical computational model for the cognitive process underlying two-alternative forced-choice decision making is the so-called drift\textendash diffusion model (DDM). In this model, a decision variable keeps track of the integrated difference in sensory evidence for two competing alternatives. Here I extend the notion of a drift\textendash diffusion process to multiple alternatives. The competition between n alternatives takes place in a linear subspace of n \textendash{} 1 dimensions; that is, there are n \textendash{} 1 decision variables, which are coupled through correlated noise sources. I derive the multiple-alternative DDM starting from a system of coupled, linear firing rate equations. I also show that a Bayesian sequential probability ratio test for multiple alternatives is, in fact, equivalent to these same linear DDMs, but with time-varying thresholds. If the original neuronal system is nonlinear, one can once again derive a model describing a lower-dimensional diffusion process. The dynamics of the nonlinear DDM can be recast as the motion of a particle on a potential, the general form of which is given analytically for an arbitrary number of alternatives.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\44GEZQYA\\Roxin - 2019 - Drift–diffusion models for multiple-alternative fo.pdf}
}

@article{roy_1953,
  title = {On a {{Heuristic Method}} of {{Test Construction}} and Its Use in {{Multivariate Analysis}}},
  author = {Roy, S. N.},
  year = {1953},
  month = jun,
  journal = {The Annals of Mathematical Statistics},
  volume = {24},
  number = {2},
  pages = {220--238},
  issn = {0003-4851},
  doi = {10.1214/aoms/1177729029},
  langid = {english},
  annotation = {00628},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HB5VQTW9\\Roy - 1953 - On a Heuristic Method of Test Construction and its.pdf}
}

@article{roy_1953a,
  title = {Simultaneous {{Confidence Interval Estimation}}},
  author = {Roy, S. N. and Bose, R. C.},
  year = {1953},
  month = dec,
  journal = {The Annals of Mathematical Statistics},
  volume = {24},
  number = {4},
  pages = {513--536},
  issn = {0003-4851},
  doi = {10.1214/aoms/1177728912},
  abstract = {The work of Neyman on confidence limits and of Fisher on fiducial limits is well known. However, in most applications the interval or limits fo only a single parameter or a single function of the parameters has been considered. Recently Scheff6 [2] and Tukey [3] have considered special cases of what may be called problems of simultaneous estimation, in which one is interested in giving confidence intervals for a finiite or infinite set of parametric functions such that the probability of the parametric functions of the set being simultaneously covered by the corresponding intervals is a preassigned number 1 - a(O {$<$} a {$<$} 1). In this paper we discuss in Section 1, a set of sufficient conditions under which such simultaneous estimation is possible, and bring out the connection of this with a method of test construction considered by one of the authors in a previous paper [1]. In Section 2 some univariate examples (including the ones due to Scheffe and Tukey) are considered from this point of view. Sections 3 to 6 are concerned with multivariate applications, giving results which are believed to be new. The associated tests all turn out to be the same as in [1] except for the example in Section 4.3 which, in a sense, is a multivariate generalization of Tukey's example (Section 2.2). Section 3 gives the notation and preliminaries for multivariate applications. Section 4 gives confidence bounds on linear functions of means for multivariate normal populations. Sections 5 and 6 give respectively confidence bounds on certain functions of the elements of population covariance matrices and population canonical regressions, from which a chain of simpler consequences would follow by the application of a set of matrix theorems. This has been partly indicated in the present paper and will be more fully discussed in a later paper.},
  langid = {english},
  annotation = {00319},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UANXZIFR\\Roy and Bose - 1953 - Simultaneous Confidence Interval Estimation.pdf}
}

@manual{rstudioteam_2021,
  type = {Manual},
  title = {{{RStudio}}: {{Integrated}} Development Environment for {{R}}},
  author = {{RStudio Team}},
  year = {2021},
  address = {{Boston, MA}},
  organization = {{RStudio, PBC}}
}

@article{rubin_1974,
  title = {Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.},
  author = {Rubin, Donald B.},
  year = {1974},
  journal = {Journal of Educational Psychology},
  volume = {66},
  number = {5},
  pages = {688--701},
  issn = {0022-0663},
  doi = {10.1037/h0037350},
  langid = {english},
  annotation = {00000},
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}

@article{rubin_1982,
  title = {{{EM}} Algorithms for {{ML}} Factor Analysis},
  author = {Rubin, Donald B. and Thayer, Dorothy T.},
  year = {1982},
  month = mar,
  journal = {Psychometrika},
  volume = {47},
  number = {1},
  pages = {69--76},
  issn = {0033-3123, 1860-0980},
  doi = {10.1007/BF02293851},
  abstract = {The details of EM algorithms for maximumlikelihood factor analysisare presented for both the exploratory and confirmatorymodels.The algorithm is essentiallythe same for both casesand involvesonly simpleleast squares regressionoperations; the largest matrix inversionrequired is for a q x q symmetricmatrix where q is the matrix of factors. The examplethat is used demonstrates that the likelihood for the factor analysis model may have multiple modes that are not simply rotations of each other; such behaviorshould concern users of maximumlikelihoodfactor analysis and certainlyshould cast doubt on the general utility of second derivativesof the log likelihoodas measuresof precisionof estimation.},
  langid = {english},
  annotation = {00689},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UQASGTGT\\Rubin und Thayer - 1982 - EM algorithms for ML factor analysis.pdf}
}

@article{rubin_2005,
  title = {Causal {{Inference Using Potential Outcomes}}: {{Design}}, {{Modeling}}, {{Decisions}}},
  shorttitle = {Causal {{Inference Using Potential Outcomes}}},
  author = {Rubin, Donald B},
  year = {2005},
  month = mar,
  journal = {Journal of the American Statistical Association},
  volume = {100},
  number = {469},
  pages = {322--331},
  issn = {0162-1459, 1537-274X},
  doi = {10.1198/016214504000001880},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UFUQR8NJ\\Rubin - 2005 - Causal Inference Using Potential Outcomes Design,.pdf}
}

@book{rudolf_2020,
  title = {Multivariate {{Verfahren}}},
  author = {Rudolf, Matthias and Buse, Johannes},
  year = {2020},
  publisher = {{Hogrefe}},
  address = {{G\"ottingen}}
}

@article{rumbold_2017,
  title = {A Critique of the Regulation of Data Science in Healthcare Research in the {{European Union}}},
  author = {Rumbold, John M. M. and Pierscionek, Barbara K.},
  year = {2017},
  month = dec,
  journal = {BMC Medical Ethics},
  volume = {18},
  number = {1},
  issn = {1472-6939},
  doi = {10.1186/s12910-017-0184-y},
  abstract = {The EU offers a suitable milieu for the comparison and harmonisation of healthcare across different languages, cultures, and jurisdictions (albeit with a supranational legal framework), which could provide improvements in healthcare standards across the bloc. There are specific ethico-legal issues with the use of data in healthcare research that mandate a different approach from other forms of research. The use of healthcare data over a long period of time is similar to the use of tissue in biobanks. There is a low risk to subjects but it is impossible to gain specific informed consent given the future possibilities for research. Large amounts of data on a subject present a finite risk of re-identification. Consequently, there is a balancing act between this risk and retaining sufficient utility of the data. Anonymising methods need to take into account the circumstances of data sharing to enable an appropriate balance in all cases. There are ethical and policy advantages to exceeding the legal requirements and thereby securing the social licence for research. This process would require the examination and comparison of data protection laws across the trading bloc to produce an ethico-legal framework compatible with the requirements of all member states. Seven EU jurisdictions are given consideration in this critique.},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RJWKDKS9\\Rumbold and Pierscionek - 2017 - A critique of the regulation of data science in he.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TGCV39DR\\Rumbold and Pierscionek - 2017 - A critique of the regulation of data science in he.pdf}
}

@article{rumbold_2017a,
  title = {The {{Effect}} of the {{General Data Protection Regulation}} on {{Medical Research}}},
  author = {Rumbold, John Mark Michael and Pierscionek, Barbara},
  year = {2017},
  month = feb,
  journal = {Journal of Medical Internet Research},
  volume = {19},
  number = {2},
  pages = {e47},
  issn = {1438-8871},
  doi = {10.2196/jmir.7108},
  abstract = {Background: The enactment of the General Data Protection Regulation (GDPR) will impact on European data science. Particular concerns relating to consent requirements that would severely restrict medical data research have been raised. Objective: Our objective is to explain the changes in data protection laws that apply to medical research and to discuss their potential impact. Methods: Analysis of ethicolegal requirements imposed by the GDPR. Results: The GDPR makes the classification of pseudonymised data as personal data clearer, although it has not been entirely resolved. Biomedical research on personal data where consent has not been obtained must be of substantial public interest. Conclusions: The GDPR introduces protections for data subjects that aim for consistency across the EU. The proposed changes will make little impact on biomedical data research.},
  langid = {english},
  annotation = {00011},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\C6EN6T3E\\Rumbold and Pierscionek - 2017 - The Effect of the General Data Protection Regulati.pdf;C\:\\Users\\dirko\\Zotero\\storage\\YF33IBU4\\Rumbold and Pierscionek - 2017 - The Effect of the General Data Protection Regulati.pdf}
}

@article{rumelhart_1986,
  title = {Learning Representations by Back-Propagating Errors},
  author = {Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
  year = {1986},
  journal = {Nature},
  number = {323},
  pages = {533--536},
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}

@article{runke_2013,
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  author = {Runke, Meridith A. and Markand, Omkar N.},
  year = {2013},
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  journal = {Clinical Neurophysiology},
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  number = {10},
  pages = {2063--2066},
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}

@book{ruschendorf_2014,
  title = {{Mathematische Statistik}},
  author = {R{\"u}schendorf, Ludger},
  year = {2014},
  series = {{Masterclass}},
  publisher = {{Springer Spektrum}},
  address = {{Berlin}},
  isbn = {978-3-642-41996-6},
  langid = {german},
  annotation = {00010  OCLC: 881753806},
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}

@book{russell_2010,
  title = {{Artificial Intelligence}},
  author = {Russell, Stuart and Norvig, Peter},
  year = {2010},
  month = apr,
  edition = {3rd revised edition.},
  publisher = {{Prentice Hall}},
  address = {{Boston}},
  abstract = {Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is "one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world." One of the other two courses, an introduction to database software, is being taught by Pearson authorDr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle (TM), NOOK (TM), and the iPhone (R)/iPad (R). To learn more about the course on artificial intelligence, visit http://www.ai-class.com. To read the full New York Timesarticle, click here.},
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  langid = {Englisch},
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}

@book{rutherford_2011,
  title = {{{ANOVA}} and {{ANCOVA A GLM Approach}}},
  author = {Rutherford, Andrew},
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}

@article{s._2002,
  title = {Movement-Related Potentials Associated with Self-Paced, Cued and Imagined Arm Movements},
  author = {S., Jankelowitz and J., Colebatch},
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  month = nov,
  journal = {Experimental Brain Research},
  volume = {147},
  number = {1},
  pages = {98--107},
  issn = {0014-4819, 1432-1106},
  doi = {10.1007/s00221-002-1220-8},
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}

@techreport{sadeghiyeh_2019,
  type = {Preprint},
  title = {Temporal Discounting Correlates with Directed Exploration but Not with Random Exploration},
  author = {Sadeghiyeh, Hashem and Wang, Siyu and Alberhasky, Maxwell R. and Kyllo, Hannah M. and Shenhav, Amitai and Wilson, Robert C.},
  year = {2019},
  month = sep,
  institution = {{Neuroscience}},
  doi = {10.1101/783498},
  abstract = {The explore-exploit dilemma describes the trade off that occurs any time we must choose between exploring unknown options and exploiting options we know well. Implicit in this trade off is how we value future rewards \textemdash{} exploiting is usually better in the short term, but in the longer term the benefits of exploration can be huge. Thus, in theory there should be a tight connection between how much people value future rewards, i.e. how much they discount future rewards relative to immediate rewards, and how likely they are to explore, with less `temporal discounting' associated with more exploration. By measuring individual differences in temporal discounting and correlating them with explore-exploit behavior, we tested whether this theoretical prediction holds in practice. We used the 27item Delay-Discounting Questionnaire to estimate temporal discounting and the Horizon Task to quantify two strategies of explore-exploit behavior: directed exploration, where information drives exploration by choice, and random exploration, where behavioral variability drives exploration by chance. We find a clear correlation between temporal discounting and directed exploration, with more temporal discounting leading to less directed exploration. Conversely, we find no relationship between temporal discounting and random exploration. Unexpectedly, we find that the relationship with directed exploration appears to be driven by a correlation between temporal discounting and uncertainty seeking at short time horizons, rather than information seeking at long horizons. Taken together our results suggest a nuanced relationship between temporal discounting and explore-exploit behavior that may be mediated by multiple factors.},
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}

@book{saint-mont_2011a,
  title = {{Statistik im Forschungsprozess}},
  author = {{Saint-Mont}, Uwe},
  year = {2011},
  publisher = {{Physica-Verlag HD}},
  address = {{Heidelberg}},
  doi = {10.1007/978-3-7908-2723-1},
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}

@article{samanez-larkin_2007,
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  month = jun,
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@article{sambrook_2015,
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@article{sampson_1992,
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  volume = {87},
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@article{samuel_1959,
  title = {Some {{Studies}} in {{Machine Learning Using}} the {{Game}} of {{Checkers}}},
  author = {Samuel, A L},
  year = {1959},
  pages = {21},
  abstract = {Two machine-learning procedures have been investigated in some detail using the game of checkers. Enough work has been done to verify the fact that a computer can be programmed so that it will learn to playa better game of checkers than can be played by the person who wrote the program. Furthermore, it can learn to do this in a remarkably short period of time 18 or 10 hours of machine-playing time) when given only the rules of the game, a sense of direction, and a redundant and incomplete Jist of parameters which are thought to have something to do with the game, but whose correct signs and relative weights are unknown and unspecified. The principles of machine learning verified by these experiments are, of course, applicable to many other situations.},
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@article{sanz-leon_2015,
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@book{sarkar_2016,
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  year = {2016},
  publisher = {{Apress}},
  address = {{Berkeley, CA}},
  doi = {10.1007/978-1-4842-2388-8},
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@book{sarkka_2013,
  ids = {sarkka_bayesian_a},
  title = {Bayesian {{Filtering}} and {{Smoothing}}},
  author = {Sarkka, Simo},
  year = {2013},
  publisher = {{Cambridge University Press}},
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  annotation = {00876},
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}

@article{sarwate_2014,
  title = {Sharing Privacy-Sensitive Access to Neuroimaging and Genetics Data: A Review and Preliminary Validation},
  shorttitle = {Sharing Privacy-Sensitive Access to Neuroimaging and Genetics Data},
  author = {Sarwate, Anand D. and Plis, Sergey M. and Turner, Jessica A. and Arbabshirani, Mohammad R. and Calhoun, Vince D.},
  year = {2014},
  month = apr,
  journal = {Frontiers in Neuroinformatics},
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  issn = {1662-5196},
  doi = {10.3389/fninf.2014.00035},
  abstract = {The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the ``small N'' problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries\textemdash the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.},
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@book{sauer_2019,
  title = {{Moderne Datenanalyse mit R: Daten einlesen, aufbereiten, visualisieren, modellieren und kommunizieren}},
  shorttitle = {{Moderne Datenanalyse mit R}},
  author = {Sauer, Sebastian},
  year = {2019},
  series = {{FOM-Edition}},
  publisher = {{Springer Fachmedien Wiesbaden}},
  address = {{Wiesbaden}},
  doi = {10.1007/978-3-658-21587-3},
  isbn = {978-3-658-21586-6 978-3-658-21587-3},
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}

@book{sauer_2019a,
  title = {{Moderne Datenanalyse mit R: Daten einlesen, aufbereiten, visualisieren, modellieren und kommunizieren}},
  shorttitle = {{Moderne Datenanalyse mit R}},
  author = {Sauer, Sebastian},
  year = {2019},
  series = {{FOM-Edition}},
  publisher = {{Springer Fachmedien Wiesbaden}},
  address = {{Wiesbaden}},
  doi = {10.1007/978-3-658-21587-3},
  isbn = {978-3-658-21586-6 978-3-658-21587-3},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\D6X28VGB\\Sauer - 2019 - Moderne Datenanalyse mit R Daten einlesen, aufber.pdf}
}

@article{saxe_2006,
  title = {Divide and Conquer: A Defense of Functional Localizers},
  shorttitle = {Divide and Conquer},
  author = {Saxe, Rebecca and Brett, Matthew and Kanwisher, Nancy},
  year = {2006},
  month = may,
  journal = {NeuroImage},
  volume = {30},
  number = {4},
  pages = {1088-1096; discussion 1097-1099},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2005.12.062},
  abstract = {Numerous functionally distinct regions of cortex (e.g., V1, MT, the fusiform face area) can be easily identified in any normal human subject in just a few minutes of fMRI scanning. However, the locations of these regions vary across subjects. Investigations of these regions have therefore often used a functional region of interest (fROI) approach in which the region is first identified functionally in each subject individually, before subsequent scans in the same subjects test specific hypotheses concerning that region. This fROI method, which resembled long-established practice in visual neurophysiology, has methodological, statistical, and theoretical advantages over standard alternatives (such as whole-brain analyses of group data): (i) because functional properties are more consistently and robustly associated with fROIs than with locations in stereotaxic space, functional hypotheses concerning fROIs are often the most straightforward to frame, motivate, and test, (ii) because hypotheses are tested in only a handful of fROIs (instead of in tens of thousands of voxels), advance specification of fROIs provides a massive increase in statistical power over whole-brain analyses, and (iii) some fROIs may serve as candidate distinct components of the mind/brain worth investigation as such. Of course fROIs can be productively used in conjunction with other complementary methods. Here, we explain the motivation for and advantages of the fROI approach, and we rebut the criticism of this method offered by Friston et al. (Friston, K., Rotshtein, P., Geng, J., Sterzer, P., Henson, R., in press. A critique of functional localizers. NeuroImage).},
  langid = {english},
  pmid = {16635578},
  keywords = {Brain Mapping,Cerebral Cortex,Cognitive neuroscience,Dominance; Cerebral,Fourier Analysis,Humans,Image Processing; Computer-Assisted,Magnetic Resonance Imaging,Mathematical Computing,Mind-Body Relations; Metaphysical,Neuroimaging,Radiosurgery,Regions of interest,Sensitivity and Specificity,Surgery; Computer-Assisted},
  annotation = {00347},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5JKRAI6C\\Saxe et al_2006_NeuroImage_Divide and conquer.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UDEXZWTM\\S1053811905025796.html}
}

@article{saxe_2021,
  title = {If Deep Learning Is the Answer, What Is the Question?},
  author = {Saxe, Andrew and Nelli, Stephanie and Summerfield, Christopher},
  year = {2021},
  month = jan,
  journal = {Nature Reviews Neuroscience},
  volume = {22},
  number = {1},
  pages = {55--67},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/s41583-020-00395-8},
  abstract = {Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This approach has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, and not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterize computations or neural codes, or who wish to understand perception, attention, memory and executive functions? In this Perspective, our goal is to offer a road map for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics and neural representations in artificial and biological systems, and we highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XBZ2FTIM\\Saxe et al. - 2021 - If deep learning is the answer, what is the questi.pdf}
}

@article{schaar_2016,
  title = {What Is Important for {{Data Protection}} in Science in the Future?},
  author = {Schaar, Katrin},
  year = {2016},
  journal = {RatSWD Working Paper Series},
  pages = {16},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3Z2W6YFV\\Schaar - RatSWD Working Paper 258.pdf}
}

@article{schaar_2017,
  title = {{Die informierte Einwilligung als Voraussetzung f\"ur die (Nach-)nutzung von Forschungsdaten}},
  author = {Schaar, Katrin},
  year = {2017},
  journal = {RatSWD Working Paper Series},
  publisher = {{German Data Forum ( RatSWD)}},
  doi = {10.17620/02671.12},
  langid = {german},
  annotation = {00008},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9MN5GG9C\\Schaar - 2017 - Die informierte Einwilligung als Voraussetzung für.pdf}
}

@book{schimel_2012,
  title = {Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded},
  shorttitle = {Writing Science},
  author = {Schimel, Joshua},
  year = {2012},
  publisher = {{Oxford University Press}},
  address = {{Oxford ; New York}},
  isbn = {978-0-19-976023-7 978-0-19-976024-4},
  langid = {english},
  lccn = {T11 .S35 2012},
  keywords = {Proposal writing for grants,Technical writing},
  annotation = {OCLC: ocn738354410},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LKFFYU3N\\Schimel - 2012 - Writing science how to write papers that get cite.pdf}
}

@article{schimmack_2000,
  title = {Dimensional {{Models}} of {{Core A}}ect: {{A Quantitative Comparison}} by {{Means}} of {{Structural Equation Modeling}}},
  author = {Schimmack, Ulrich and Grob, Alexander},
  year = {2000},
  pages = {21},
  abstract = {The present article compares dimensional models of aect with each other. The article focuses on the pleasure{$\pm$}arousal model, the energetic and tense arousal model, and a three-dimensional model with separate pleasure{$\pm$}displeasure, awake{$\pm$}tiredness, and tension{$\pm$}relaxation dimensions. The results show that the three-dimensional model cannot be reduced to a two-dimensional model. Problems of the two-dimensional models' reductionism are discussed. We conclude that a three-dimensional description of aect is necessary. However, the three-dimensional model is not sucient to account for all aspects of the structure of aect. Copyright \# 2000 John Wiley \& Sons, Ltd.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EXYARFHW\\1099-0984(200007_08)14_4_325__AID-PER380_3.0.CO\;2-I.pdf}
}

@article{schlegel_,
  title = {Somatosensory Mismatch Negativity in a {{Markov}} Process Paradigm},
  author = {Schlegel, Alexander},
  pages = {9},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PC5BBFKQ\\Schlegel - Somatosensory mismatch negativity in a Markov proc.pdf}
}

@article{schmidhuber_2015,
  title = {Deep Learning in Neural Networks: {{An}} Overview},
  shorttitle = {Deep Learning in Neural Networks},
  author = {Schmidhuber, J{\"u}rgen},
  year = {2015},
  month = jan,
  journal = {Neural Networks},
  volume = {61},
  pages = {85--117},
  issn = {0893-6080},
  doi = {10.1016/j.neunet.2014.09.003},
  abstract = {In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning \& evolutionary computation, and indirect search for short programs encoding deep and large networks.},
  keywords = {Deep learning,Evolutionary computation,Reinforcement learning,Supervised learning,Unsupervised learning},
  annotation = {02642},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5ZI7AHTC\\Schmidhuber_2015_Neural Networks_Deep learning in neural networks.pdf;C\:\\Users\\dirko\\Zotero\\storage\\DA4QE5XE\\S0893608014002135.html}
}

@book{schmidt_2009,
  title = {{Ma\ss{} und Wahrscheinlichkeit}},
  author = {Schmidt, Klaus D.},
  year = {2009},
  series = {{Springer-Lehrbuch}},
  publisher = {{Springer}},
  address = {{Berlin}},
  abstract = {Die Wahrscheinlichkeitstheorie hat durch vielf\"altige neue Anwendungen in der Wirtschaft auch in der Lehre deutlich an Bedeutung gewonnen. Sie beruht auf der Mass- und Integrationstheorie, die gleichzeitig eine der Grundlagen der Funktionalanalysis bildet. Dieses Buch bietet eine Einf\"uhrung in die Wahrscheinlichkeitstheorie im Spannungsfeld zwischen ihren theoretischen Grundlagen und ihren Anwendungen. Dabei wird die systematische Darstellung der klassischen Themen der Wahrscheinlichkeitstheorie durch zahlreiche Beispiele und Aufgaben erg\"anzt, die Ansatzpunkte f\"ur eine Vertiefung der Theorie und f\"ur Anwendungen beispielsweise in der Statistik und in der Versicherungsmathematik darstellen. ... Geschrieben f\"ur: Studierende der Mathematik und Wirtschaftsmathematik. (3)},
  isbn = {978-3-540-89729-3 978-3-540-89730-9},
  langid = {german},
  annotation = {00054  OCLC: 351304848},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UHQFL769\\Schmidt - 2009 - Maß und Wahrscheinlichkeit.pdf}
}

@article{schmidt_2014,
  title = {Imaging Tactile Imagery: {{Changes}} in Brain Connectivity Support Perceptual Grounding of Mental Images in Primary Sensory Cortices},
  shorttitle = {Imaging Tactile Imagery},
  author = {Schmidt, Timo Torsten and Ostwald, Dirk and Blankenburg, Felix},
  year = {2014},
  month = sep,
  journal = {NeuroImage},
  volume = {98},
  pages = {216--224},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2014.05.014},
  abstract = {Constructing mental representations in the absence of sensory stimulation is a fundamental ability of the human mind and has been investigated in numerous brain imaging studies. However, it is still unclear how brain areas facilitating mental construction processes interact with brain regions related to specific sensory representations. In this fMRI study subjects formed mental representations of tactile stimuli either from memory (imagery) or from presentation of actual corresponding vibrotactile patterned stimuli. First our analysis addressed the question of whether tactile imagery recruits primary somatosensory cortex (SI), because the activation of early perceptual areas is classically interpreted as perceptual grounding of the mental image. We also tested whether a network, referred to as `core construction system', is involved in the generation of mental representations in the somatosensory domain. In fact, we observed imagery-induced activation of SI. We further found support for the notion of a modality independent construction network with the retrosplenial cortices and the precuneus as core components, which were supplemented with the left inferior frontal gyrus (IFG). Finally, psychophysiological interaction (PPI) analyses revealed robust imagery-modulated changes in the connectivity of these construction related areas, which suggests that they orchestrate the assembly of an abstract mental representation. Interestingly, we found increased coupling between prefrontal cortex (left IFG) and SI during mental imagery, indicating the augmentation of an abstract mental representation by reactivating perceptually grounded sensory details.},
  langid = {english},
  annotation = {00017},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9NETD4F7\\Schmidt et al. - 2014 - Imaging tactile imagery Changes in brain connecti.pdf}
}

@article{schmidt_2021,
  title = {Purchasing under Threat: {{Changes}} in Shopping Patterns during the {{COVID-19}} Pandemic},
  shorttitle = {Purchasing under Threat},
  author = {Schmidt, Sebastian and Benke, Christoph and {Pan{\'e}-Farr{\'e}}, Christiane A.},
  editor = {Andreatta, Marta},
  year = {2021},
  month = jun,
  journal = {PLOS ONE},
  volume = {16},
  number = {6},
  pages = {e0253231},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0253231},
  abstract = {The spreading of COVID-19 has led to panic buying all over the world. In this study, we applied an animal model framework to elucidate changes in human purchasing behavior under COVID-19 pandemic conditions. Purchasing behavior and potential predictors were assessed in an online questionnaire format (               N               = 813). Multiple regression analyses were used to evaluate the role of individually               Perceived Threat of COVID-19               , anxiety related personality traits (trait-anxiety, intolerance of uncertainty) and the role of media exposure in predicting quantity and frequency of purchasing behavior. High levels of               Perceived Threat of COVID-19               were associated significantly with a reported reduction in purchasing frequency (               b               = -.24,               p               {$<$} .001) and an increase in the quantity of products bought per purchase (               b               = .22,               p               {$<$} .001). These results are comparable to observed changes in foraging behavior in rodents under threat conditions. Higher levels of intolerance of uncertainty (               b               = .19,               p               {$<$} .001) and high extend of media exposure (               b               = .27,               p               {$<$} .001) were positively associated with               Perceived Threat of COVID-19               and an increase in purchasing quantity. This study contributes to our understanding of aberrated human purchasing behavior and aims to link findings from animal research to human behavior beyond experimental investigations.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZEFHXD46\\Schmidt et al. - 2021 - Purchasing under threat Changes in shopping patte.pdf}
}

@book{schmitt_2013,
  title = {Echo-{{Planar Imaging Theory}}, {{Technique}} and {{Application}}},
  author = {Schmitt, F and Stehling, M. K and Turner, Robert and Mansfield, P},
  year = {2013},
  publisher = {{Springer Berlin}},
  address = {{Berlin}},
  isbn = {978-3-642-80445-8},
  langid = {english},
  annotation = {00010  OCLC: 864568455},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QNE9FX4H\\Schmitt et al. - 2013 - Echo-Planar Imaging Theory, Technique and Applicat.pdf}
}

@article{schneider_,
  title = {{Gew\"ohnliche Differentialgleichungen}},
  author = {Schneider, Albert},
  pages = {611},
  langid = {german},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3MSCQSEI\\Schneider - Gewöhnliche Differentialgleichungen.pdf}
}

@book{scholkopf_2002,
  ids = {scholkopf_learning_2002a},
  title = {Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond},
  shorttitle = {Learning with Kernels},
  author = {Sch{\"o}lkopf, Bernhard and Smola, Alexander J.},
  year = {2002},
  series = {Adaptive Computation and Machine Learning},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-19475-4},
  langid = {english},
  lccn = {Q325.5 .S32 2002},
  keywords = {Kernel functions,Support vector machines},
  annotation = {13438},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DSKXAEIQ\\Schölkopf and Smola - 2002 - Learning with kernels support vector machines, re.pdf}
}

@article{scholz_2014,
  title = {Applications of the {{Noncentral}} t\textendash{{Distribution}}},
  author = {Scholz, Fritz},
  year = {2014},
  pages = {58},
  langid = {english},
  annotation = {00008},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8WHWKH5Q\\Scholz - Applications of the Noncentral t–Distribution.pdf;C\:\\Users\\dirko\\Zotero\\storage\\T5638ANE\\Scholz - Applications of the Noncentral t–Distribution.pdf}
}

@article{schonbrodt_2017,
  title = {Der {{Umgang}} Mit {{Forschungsdaten}} Im {{Fach Psychologie}}: {{Konkretisierung}} Der {{DFG-Leitlinien}}},
  shorttitle = {Der {{Umgang}} Mit {{Forschungsdaten}} Im {{Fach Psychologie}}},
  author = {Sch{\"o}nbrodt, Felix and Gollwitzer, Mario and {Abele-Brehm}, Andrea},
  year = {2017},
  journal = {Psychologische Rundschau},
  annotation = {00005},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5I3MFWHP\\Nuijten, Hartgerink et al 2016 - The prevalence of statistical reporting.pdf}
}

@article{schonbrodt_2017a,
  title = {{Der Umgang mit Forschungsdaten im Fach Psychologie: Konkretisierung der DFG-Leitlinien: Im Auftrag des DGPs Vorstands (17. 09. 2016)}},
  shorttitle = {{Der Umgang mit Forschungsdaten im Fach Psychologie}},
  author = {Sch{\"o}nbrodt, Felix and Gollwitzer, Mario and {Abele-Brehm}, Andrea},
  year = {2017},
  month = jan,
  journal = {Psychologische Rundschau},
  volume = {68},
  number = {1},
  pages = {20--35},
  issn = {0033-3042, 2190-6238},
  doi = {10.1026/0033-3042/a000341},
  langid = {german},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2FPEI3EV\\Schönbrodt et al. - 2017 - Der Umgang mit Forschungsdaten im Fach Psychologie.pdf;C\:\\Users\\dirko\\Zotero\\storage\\2LGHVGDR\\Schönbrodt et al. - 2017 - Der Umgang mit Forschungsdaten im Fach Psychologie.pdf;C\:\\Users\\dirko\\Zotero\\storage\\94EZKMFB\\Schönbrodt et al. - 2017 - Der Umgang mit Forschungsdaten im Fach Psychologie.pdf;C\:\\Users\\dirko\\Zotero\\storage\\FBSYZ8CD\\Schönbrodt et al. - 2017 - Der Umgang mit Forschungsdaten im Fach Psychologie.pdf;C\:\\Users\\dirko\\Zotero\\storage\\KY2FSEGA\\Schönbrodt et al. - 2017 - Der Umgang mit Forschungsdaten im Fach Psychologie.pdf;C\:\\Users\\dirko\\Zotero\\storage\\Q5G7VTSB\\Schönbrodt et al. - 2017 - Der Umgang mit Forschungsdaten im Fach Psychologie.pdf}
}

@article{schrittwieser_2020,
  title = {Mastering {{Atari}}, {{Go}}, {{Chess}} and {{Shogi}} by {{Planning}} with a {{Learned Model}}},
  author = {Schrittwieser, Julian and Antonoglou, Ioannis and Hubert, Thomas and Simonyan, Karen and Sifre, Laurent and Schmitt, Simon and Guez, Arthur and Lockhart, Edward and Hassabis, Demis and Graepel, Thore and Lillicrap, Timothy and Silver, David},
  year = {2020},
  month = feb,
  journal = {arXiv:1911.08265 [cs, stat]},
  eprint = {1911.08265},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {00086},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2VKJU9S4\\Schrittwieser et al. - 2020 - Mastering Atari, Go, Chess and Shogi by Planning w.pdf}
}

@article{schuck_2016,
  title = {Human {{Orbitofrontal Cortex Represents}} a {{Cognitive Map}} of {{State Space}}},
  author = {Schuck, Nicolas W. and Cai, Ming Bo and Wilson, Robert C. and Niv, Yael},
  year = {2016},
  journal = {Neuron},
  volume = {91},
  number = {6},
  pages = {1402--1412},
  issn = {10974199},
  doi = {10.1016/j.neuron.2016.08.019},
  abstract = {Although the orbitofrontal cortex (OFC) has been studied intensely for decades, its precise functions have remained elusive. We recently hypothesized that the OFC contains a ``cognitive map'' of task space in which the current state of the task is represented, and this representation is especially critical for behavior when states are unobservable from sensory input. To test this idea, we apply pattern-classification techniques to neuroimaging data from humans performing a decision-making task with 16 states. We show that unobservable task states can be decoded from activity in OFC, and decoding accuracy is related to task performance and the occurrence of individual behavioral errors. Moreover, similarity between the neural representations of consecutive states correlates with behavioral accuracy in corresponding state transitions. These results support the idea that OFC represents a cognitive map of task space and establish the feasibility of decoding state representations in humans using non-invasive neuroimaging.},
  pmid = {27657452},
  annotation = {00112}
}

@article{schuck_2017,
  ids = {schuck_state_2017-1},
  title = {A State Representation for Reinforcement Learning and Decision-Making in the Orbitofrontal Cortex},
  author = {Schuck, Nicolas W. and Wilson, Robert C. and Niv, Yael},
  year = {2017},
  month = nov,
  doi = {10.1101/210591},
  abstract = {Despite decades of research, the exact ways in which the orbitofrontal cortex (OFC) influences cognitive function have remained mysterious. Anatomically, the OFC is characterized by remarkably broad connectivity to sensory, limbic and subcortical areas, and functional studies have implicated the OFC in a plethora of functions ranging from facial processing to value-guided choice. Notwithstanding such diversity of findings, much research suggests that one important function of the OFC is to support decision making and reinforcement learning. Here, we describe a novel theory that posits that OFC's specific role in decision-making is to provide an up-to-date representation of task-related information, called a state representation. This representation reflects a mapping between distinct task states and sensory as well as unobservable information. We summarize evidence supporting the existence of such state representations in rodent and human OFC and argue that forming these state representations provides a crucial scaffold that allows animals to efficiently perform decision making and reinforcement learning in high-dimensional and partially observable environments. Finally, we argue that our theory offers an integrating framework for linking the diversity of functions ascribed to OFC and is in line with its wide ranging connectivity.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GXJPLQWL\\SchuckEtAl2017.pdf}
}

@article{schuck_2019,
  title = {Sequential Replay of Nonspatial Task States in the Human Hippocampus},
  author = {Schuck, Nicolas W. and Niv, Yael},
  year = {2019},
  month = jun,
  journal = {Science},
  volume = {364},
  number = {6447},
  pages = {eaaw5181},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.aaw5181},
  abstract = {Sequential neural activity patterns related to spatial experiences are ``replayed'' in the hippocampus of rodents during rest. We investigated whether replay of nonspatial sequences can be detected noninvasively in the human hippocampus. Participants underwent functional magnetic resonance imaging (fMRI) while resting after performing a decision-making task with sequential structure. Hippocampal fMRI patterns recorded at rest reflected sequentiality of previously experienced task states, with consecutive patterns corresponding to nearby states. Hippocampal sequentiality correlated with the fidelity of task representations recorded in the orbitofrontal cortex during decision-making, which were themselves related to better task performance. Our findings suggest that hippocampal replay may be important for building representations of complex, abstract tasks elsewhere in the brain and establish feasibility of investigating fast replay signals with fMRI.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5FAX3TXL\\Schuck und Niv - 2019 - Sequential replay of nonspatial task states in the.pdf}
}

@article{schultz_2015,
  title = {Neuronal {{Reward}} and {{Decision Signals}}: {{From Theories}} to {{Data}}},
  shorttitle = {Neuronal {{Reward}} and {{Decision Signals}}},
  author = {Schultz, Wolfram},
  year = {2015},
  month = jul,
  journal = {Physiological Reviews},
  volume = {95},
  number = {3},
  pages = {853--951},
  issn = {0031-9333},
  doi = {10.1152/physrev.00023.2014},
  abstract = {Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.},
  annotation = {00252},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QAA3SILR\\Schultz_2015_Physiological Reviews_Neuronal Reward and Decision Signals.pdf;C\:\\Users\\dirko\\Zotero\\storage\\E5IBF7LF\\physrev.00023.html}
}

@article{schwartenbeck_2013,
  title = {Exploration, Novelty, Surprise, and Free Energy Minimization},
  author = {Schwartenbeck, Philipp and FitzGerald, Thomas and Dolan, Raymond J. and Friston, Karl},
  year = {2013},
  journal = {Frontiers in Psychology},
  volume = {4},
  number = {OCT},
  pages = {1--5},
  issn = {16641078},
  doi = {10.3389/fpsyg.2013.00710},
  abstract = {This paper reviews recent developments under the free energy principle that introduce a normative perspective on classical economic (utilitarian) decision-making based on (active) Bayesian inference. It has been suggested that the free energy principle precludes novelty and complexity, because it assumes that biological systems-like ourselves-try to minimize the long-term average of surprise to maintain their homeostasis. However, recent formulations show that minimizing surprise leads naturally to concepts such as exploration and novelty bonuses. In this approach, agents infer a policy that minimizes surprise by minimizing the difference (or relative entropy) between likely and desired outcomes, which involves both pursuing the goal-state that has the highest expected utility (often termed "exploitation") and visiting a number of different goal-states ("exploration"). Crucially, the opportunity to visit new states increases the value of the current state. Casting decision-making problems within a variational framework, therefore, predicts that our behavior is governed by both the entropy and expected utility of future states. This dissolves any dialectic between minimizing surprise and exploration or novelty seeking.},
  pmid = {24109469},
  keywords = {Active inference,Exploitation,Exploration,Free energy,Novelty,Reinforcement learning},
  annotation = {00078}
}

@article{schwartenbeck_2015,
  title = {The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes},
  author = {Schwartenbeck, Philipp and FitzGerald, Thomas H.B. and Mathys, Christoph and Dolan, Ray and Friston, Karl},
  year = {2015},
  journal = {Cerebral Cortex},
  volume = {25},
  number = {10},
  pages = {3434--3445},
  issn = {14602199},
  doi = {10.1093/cercor/bhu159},
  abstract = {Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing.},
  pmid = {25056572},
  keywords = {Active inference,Confidence,Dopamine,Neuroeconomics,Precision},
  annotation = {00082}
}

@article{schwartenbeck_2016,
  title = {Neural Signals Encoding Shifts in Beliefs},
  author = {Schwartenbeck, Philipp and FitzGerald, Thomas H.B. and Dolan, Ray},
  year = {2016},
  journal = {NeuroImage},
  volume = {125},
  pages = {578--586},
  issn = {10959572},
  doi = {10.1016/j.neuroimage.2015.10.067},
  abstract = {Dopamine is implicated in a diverse range of cognitive functions including cognitive flexibility, task switching, signalling novel or unexpected stimuli as well as advance information. There is also longstanding line of thought that links dopamine with belief formation and, crucially, aberrant belief formation in psychosis. Integrating these strands of evidence would suggest that dopamine plays a central role in belief updating and more specifically in encoding of meaningful information content in observations. The precise nature of this relationship has remained unclear. To directly address this question we developed a paradigm that allowed us to decompose two distinct types of information content, information-theoretic surprise that reflects the unexpectedness of an observation, and epistemic value that induces shifts in beliefs or, more formally, Bayesian surprise. Using functional magnetic-resonance imaging in humans we show that dopamine-rich midbrain regions encode shifts in beliefs whereas surprise is encoded in prefrontal regions, including the pre-supplementary motor area and dorsal cingulate cortex. By linking putative dopaminergic activity to belief updating these data provide a link to false belief formation that characterises hyperdopaminergic states associated with idiopathic and drug induced psychosis.},
  pmid = {26520774},
  keywords = {Belief formation,Dopamine,Epistemic value,Information theory,Psychosis},
  annotation = {00028}
}

@article{schwarz_1978,
  ids = {schwarz1978,schwarz_estimating_1978-1},
  title = {Estimating the {{Dimension}} of a {{Model}}},
  author = {Schwarz, Gideon},
  year = {1978},
  month = mar,
  journal = {The Annals of Statistics},
  volume = {6},
  number = {2},
  pages = {461--464},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0090-5364, 2168-8966},
  abstract = {The problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution.},
  langid = {english},
  mrnumber = {MR468014},
  zmnumber = {0379.62005},
  keywords = {Akaike information criterion,asymptotics,Dimension},
  annotation = {34496},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CXP9PSFE\\Schwarz - 1978 - Estimating the Dimension of a Model.pdf}
}

@book{schwarzer_2015,
  title = {Meta-Analysis with {{R}}},
  author = {Schwarzer, Guido and Carpenter, James R. and R{\"u}cker, Gerta},
  year = {2015},
  series = {Use {{R}}!},
  publisher = {{Springer}},
  address = {{Cham Heidelberg New York Dordrecht London}},
  isbn = {978-3-319-21415-3 978-3-319-21416-0},
  langid = {english},
  annotation = {00227  OCLC: 929984127},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\IKBYPXY5\\Schwarzer et al. - 2015 - Meta-analysis with R.pdf}
}

@article{schwen_2020,
  title = {Ten Simple Rules for Typographically Appealing Scientific Texts},
  author = {Schwen, Lars Ole},
  editor = {Markel, Scott},
  year = {2020},
  month = dec,
  journal = {PLOS Computational Biology},
  volume = {16},
  number = {12},
  pages = {e1008458},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1008458},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4WAY39R5\\Schwen - 2020 - Ten simple rules for typographically appealing sci.pdf}
}

@article{schwobel_2021,
  title = {Balancing Control: {{A Bayesian}} Interpretation of Habitual and Goal-Directed Behavior},
  shorttitle = {Balancing Control},
  author = {Schw{\"o}bel, Sarah and Markovi{\'c}, Dimitrije and Smolka, Michael N. and Kiebel, Stefan J.},
  year = {2021},
  month = feb,
  journal = {Journal of Mathematical Psychology},
  volume = {100},
  pages = {102472},
  issn = {00222496},
  doi = {10.1016/j.jmp.2020.102472},
  abstract = {In everyday life, our behavior varies on a continuum from automatic and habitual to deliberate and goal-directed. Recent evidence suggests that habit formation and relearning of habits operate in a context-dependent manner: Habit formation is promoted when actions are performed in a specific context, while breaking off habits is facilitated after a context change. It is an open question how one can computationally model the brain's balancing between context-specific habits and goal-directed actions. Here, we propose a hierarchical Bayesian approach for control of a partially observable Markov decision process that enables conjoint learning of habits and reward structure in a contextspecific manner. In this model, habit learning corresponds to an updating of priors over policies and interacts with the learning of the outcome contingencies. Importantly, the model is solely built on probabilistic inference, which effectively provides a simple explanation of how the brain may balance contributions of habitual and goal-directed control. We illustrated the resulting behavior using agentbased simulated experiments, where we replicated several findings of devaluation, extinction, and renewal experiments, as well as the so-called two-step task which is typically used with human participants. In addition, we show how a single parameter, the habitual tendency, can explain individual differences in habit learning and the balancing between habitual and goal-directed control. Finally, we discuss the link of the proposed model to other habit learning models and implications for understanding specific phenomena in substance use disorder.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Q7D6BPGE\\Schwöbel et al. - 2021 - Balancing control A Bayesian interpretation of ha.pdf}
}

@article{scott_1958,
  title = {Foundational Aspects of Theories of Measurement},
  author = {Scott, Dana and Suppes, Patrick},
  year = {1958},
  month = jun,
  journal = {Journal of Symbolic Logic},
  volume = {23},
  number = {2},
  pages = {113--128},
  issn = {0022-4812, 1943-5886},
  doi = {10.2307/2964389},
  abstract = {It is a scientific platitude that there can be neither precise control nor prediction of phenomena without measurement. Disciplines are diverse as cosmology and social psychology provide evidence that it is nearly useless to have an exactly formulated quantitative theory if empirically feasible methods of measurement cannot be developed for a substantial portion of the quantitative concepts of the theory. Given a physical concept like that of mass or a psychological concept like that of habit strength, the point of a theory of measurement is to lay bare the structure of a collection of empirical relations which may be used to measure the characteristic of empirical phenomena corresponding to the concept. Why a collection of relations? From an abstract standpoint a set of empirical data consists of a collection of relations between specified objects. For example, data on the relative weights of a set of physical objects are easily represented by an ordering relation on the set; additional data, and a fortiori an additional relation, are needed to yield a satisfactory quantitative measurement of the masses of the objects.             The major source of difficulty in providing an adequate theory of measurement is to construct relations which have an exact and reasonable numerical interpretation and yet also have a technically practical empirical interpretation. The classical analyses of the measurement of mass, for instance, have the embarrassing consequence that the basic set of objects measured must be infinite. Here the relations postulated have acceptable numerical interpretations, but are utterly unsuitable empirically. Conversely, as we shall see in the last section of this paper, the structure of relations which have a sound empirical meaning often cannot be succinctly characterized so as to guarantee a desired numerical interpretation.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\C7F7CU5M\\Scott und Suppes - 1958 - Foundational aspects of theories of measurement.pdf}
}

@article{scott_1979,
  ids = {scott_optimal_,scott_optimal_a},
  title = {On Optimal and Data-Based Histograms},
  author = {Scott, David W},
  year = {1979},
  pages = {6},
  abstract = {In this paper the formula for the optimal histogram bin width is derived which asymptotically minimizes the integrated mean squared error. Monte Carlo methods are used to verify the usefulness of this formula for small samples. A data-based procedure for choosing the bin width parameter is proposed, which assumes a Gaussian reference standard and requires only the sample size and an estimate of the standard deviation. The sensitivity of the procedure is investigated using several probability models which violate the Gaussian assumption.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\I9X7T8GH\\Scott - On optimal and data-based histograms.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PLGWR8VM\\Scott - On optimal and data-based histograms.pdf;C\:\\Users\\dirko\\Zotero\\storage\\UKVMCVIP\\Scott - On optimal and data-based histograms.pdf}
}

@article{scott_1979a,
  title = {On Optimal and Data-Based Histograms},
  author = {Scott, David W.},
  year = {1979},
  journal = {Biometrika},
  volume = {66},
  number = {3},
  pages = {605--610},
  issn = {0006-3444, 1464-3510},
  doi = {10.1093/biomet/66.3.605},
  abstract = {In thispaper the formulaforthe optimalhistogrambin widthis derivedwhichasymptotically minimizesthe integratedmean squared error.Monte Carlo methodsare used to verify the usefulnessofthisformulaforsmall samples. A data-based procedureforchoosingthe bin widthparameteris proposed,whichassumes a Gaussian referencestandard and requiresonly the sample size and an estimateofthe standard deviation. The sensitivityofthe procedureis investigatedusingseveral probabilitymodelswhichviolate the Gaussian assumption.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\T4MQPA3W\\Scott - 1979 - On optimal and data-based histograms.pdf}
}

@article{scott_2022,
  title = {Foundational {{Aspects}} of {{Theories}} of {{Measurement}}},
  author = {Scott, Dana and Suppes, Patrick},
  year = {2022},
  pages = {17},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XJ78XVZI\\Scott und Suppes - 2022 - Foundational Aspects of Theories of Measurement.pdf}
}

@book{searle_1982,
  title = {Matrix {{Algebra Useful}} for {{Statistics}}},
  author = {Searle, Shayle},
  year = {1982},
  publisher = {{Wiley-Interscience}},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\65FZG5NK\\Searle - 1982 - Matrix Algebra Useful for Statistics.pdf}
}

@book{searle_2006,
  title = {Variance {{Components}}},
  author = {Searle},
  year = {2006},
  annotation = {00000},
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}

@article{sebastiani_1991,
  title = {Mathematical Principles of Basic Magnetic Resonance Imaging in Medicine},
  author = {Sebastiani, Giovanni and Barone, Piero},
  year = {1991},
  month = nov,
  journal = {Signal Processing},
  volume = {25},
  number = {2},
  pages = {227--250},
  issn = {01651684},
  doi = {10.1016/0165-1684(91)90065-Q},
  abstract = {In this paper we review, from a mathematical point of view, the main techniques of magnetic resonance imaging (MRI) used in medicine. A detailed mathematical description of the basic physics and of the image formation process is given in the framework of the classical theory.},
  langid = {english},
  annotation = {00031},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XLPRC2SY\\Sebastiani_Barone_1991_Signal Processing_Mathematical principles of basic magnetic resonance imaging in medicine.pdf}
}

@book{seber_2003,
  title = {Linear Regression Analysis},
  author = {Seber, G. A. F. and Lee, A. J.},
  year = {2003},
  series = {Wiley Series in Probability and Statistics},
  edition = {2nd ed},
  publisher = {{Wiley-Interscience}},
  address = {{Hoboken, N.J}},
  isbn = {978-0-471-41540-4},
  langid = {english},
  lccn = {QA278.2 .S4 2003},
  keywords = {Regression analysis},
  annotation = {OCLC: ocm51635479},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FATNMFE6\\Seber und Lee - 2003 - Linear regression analysis.pdf}
}

@book{seber_2015,
  title = {The {{Linear Model}} and {{Hypothesis}}},
  author = {Seber, George},
  year = {2015},
  series = {Springer {{Series}} in {{Statistics}}},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-319-21930-1},
  isbn = {978-3-319-21929-5 978-3-319-21930-1},
  langid = {english},
  annotation = {00003},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\4EBP2FDE\\Seber - 2015 - The Linear Model and Hypothesis.pdf}
}

@mastersthesis{selboe_2015,
  title = {Implementation and {{Adaptation}} of a {{System}} for {{Automatic Classification}} of {{Birdsong}}.},
  author = {Selboe, Kristian},
  year = {2015},
  school = {NTNU},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2Z9PCXR6\\Bilmes_Gentle_EM_Tutorial_98.pdf}
}

@article{sen_1984,
  title = {Aspects of {{Multivariate Statistical Theory}}.},
  author = {Sen, P. K. and Muirhead, Robb J.},
  year = {1984},
  month = mar,
  journal = {Journal of the American Statistical Association},
  volume = {79},
  number = {385},
  pages = {231},
  issn = {01621459},
  doi = {10.2307/2288369},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\38Q6UW7B\\Sen and Muirhead - 1984 - Aspects of Multivariate Statistical Theory..pdf}
}

@book{shadish_2001,
  title = {Experimental and Quasi-Experimental Designs for Generalized Causal Inference},
  author = {Shadish, William R. and Cook, Thomas D. and Campbell, Donald T.},
  year = {2001},
  publisher = {{Houghton Mifflin}},
  address = {{Boston}},
  isbn = {978-0-395-61556-0},
  langid = {english},
  lccn = {BD591 .S48 2001},
  keywords = {Causation,Experiments},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RPQ7FHPX\\Shadish et al. - 2001 - Experimental and quasi-experimental designs for ge.pdf}
}

@article{shaffer_,
  title = {Multiple {{Hypothesis Testing}}},
  author = {Shaffer, J P},
  journal = {Annu. Rev. Psychol.},
  pages = {24},
  abstract = {INTRODUCTION ................. ................................................ ........................ ........ .................... 561 ORGANIZING CONCEPTS ..... ................................................................................ ................ 564 Primary Hypotheses. Closure, Hierarchical Sets. andMinimal Hypotheses...................... 564 Families................................................................................................................................ 565 Type I Error Control.................................... .... ............ ............................ .... ........................ 566 Power........... ........ .... ........ .................... ........ .................................................... .... ................ 567 P-Values andAdjusted P- Values ................. .... .................................................................... 568 Closed Test Procedures ............... ........................................................................................ 569 METHODS BASED ON ORDERED P-VALUES ............... .... ................................................ 569 Methods Based on the First-Order Bonferroni Inequality .................................................. 569 Methods Based on the Simes Equality ..................... ........................ .................................... 570 Modificationsfor Logically Related Hypotheses................................................................. 571 Methods Controlling the False Discovery Rate....................... ................ ............................ 572 COMPARING NORMALLY DISTRIBUTED MEANS ............................................. ............ 573 OTHER ISSUES .................................................................................... .................................... 575 Tests vs Confidence Intervals... ............................................................................................ 575 Directional vs Nondirectional Inference ................. ........ .................................................... 576 Robustness........................................................................ ............ ........ ............ .... ........ ........ 577 Others.... ... ........................................................................................ ........................ ............ 578 CONCLUSION ...................................................... .................................................................... 580 INTRODUCTION Multiple testing refers to the testing of more than one hypothesis at a time. It is a subfield of the broader field of multiple inference, or simultaneous inference, which includes multiple estimation as well as testing. This review concentrates on testing and deals with the special problems arising from the multiple aspect. The term "multiple comparisons" has come to be used synonymously with},
  langid = {english},
  annotation = {01485},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\28NFFEXM\\Shaffer - Multiple Hypothesis Testing.pdf}
}

@article{shah_2018,
  title = {Use Our Personal Data for the Common Good},
  author = {Shah, Hetan},
  year = {2018},
  month = apr,
  journal = {Nature},
  volume = {556},
  number = {7699},
  pages = {7--7},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/d41586-018-03912-z},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\K4NRANB5\\Shah - 2018 - Use our personal data for the common good.pdf;C\:\\Users\\dirko\\Zotero\\storage\\QUMMUM3D\\Shah - 2018 - Use our personal data for the common good.pdf}
}

@article{shanmugam_2018,
  title = {Elements of Causal Inference: Foundations and Learning Algorithms},
  shorttitle = {Elements of Causal Inference},
  author = {Shanmugam, Ramalingam},
  year = {2018},
  month = nov,
  journal = {Journal of Statistical Computation and Simulation},
  volume = {88},
  number = {16},
  pages = {3248--3248},
  issn = {0094-9655, 1563-5163},
  doi = {10.1080/00949655.2018.1505197},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5YVCVHL7\\Shanmugam - 2018 - Elements of causal inference foundations and lear.pdf}
}

@article{shannon_1998,
  title = {Communication in the {{Presence}} of {{Noise}}},
  author = {Shannon, Claude E},
  year = {1998},
  journal = {PROCEEDINGS OF THE IEEE},
  volume = {86},
  number = {2},
  pages = {11},
  langid = {english},
  annotation = {08279},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\U8DGJ6A2\\shannonpaper.pdf}
}

@book{shao_2003,
  ids = {shao_mathematical_2003a},
  title = {Mathematical Statistics},
  author = {Shao, Jun},
  year = {2003},
  series = {Springer Texts in Statistics},
  edition = {2nd ed},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-95382-3},
  lccn = {QA276 .S458 2003},
  keywords = {Mathematical statistics},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\M5LIU9NJ\\Jun_Shao_Mathematical_statistics__2003.pdf;C\:\\Users\\dirko\\Zotero\\storage\\WQDDUB4M\\Shao - 2003 - Mathematical statistics.pdf}
}

@article{shen_2014,
  title = {Risk-{{Sensitive Reinforcement Learning}}},
  author = {Shen, Yun and Tobia, Michael J. and Sommer, Tobias and Obermayer, Klaus},
  year = {2014},
  month = apr,
  journal = {Neural Computation},
  volume = {26},
  number = {7},
  pages = {1298--1328},
  issn = {0899-7667},
  doi = {10.1162/NECO_a_00600},
  abstract = {We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When appropriate utility functions are chosen, the agents' behaviors express key features of human behavior as predicted by prospect theory (Kahneman \& Tversky, 1979), for example, different risk preferences for gains and losses, as well as the shape of subjective probability curves. We derive a risk-sensitive Q-learning algorithm, which is necessary for modeling human behavior when transition probabilities are unknown, and prove its convergence. As a proof of principle for the applicability of the new framework, we apply it to quantify human behavior in a sequential investment task. We find that the risk-sensitive variant provides a significantly better fit to the behavioral data and that it leads to an interpretation of the subject's responses that is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals shows a significant correlation of the risk-sensitive TD error with BOLD signal change in the ventral striatum. In addition we find a significant correlation of the risk-sensitive Q-values with neural activity in the striatum, cingulate cortex, and insula that is not present if standard Q-values are used.},
  annotation = {00023},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\TT3QTF7Q\\NECO_a_00600.html}
}

@article{sheynin_1995,
  title = {Helmert's Work in the Theory of Errors},
  author = {Sheynin, Oscar},
  year = {1995},
  journal = {Archive for History of Exact Sciences},
  volume = {49},
  number = {1},
  pages = {73--104},
  issn = {0003-9519, 1432-0657},
  doi = {10.1007/BF00374700},
  langid = {english},
  annotation = {00037},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\S99ZXX5W\\Sheynin - 1995 - Helmert's work in the theory of errors.pdf}
}

@article{sheynin_2015,
  title = {Testing the Role of Reward and Punishment Sensitivity in Avoidance Behavior: {{A}} Computational Modeling Approach},
  shorttitle = {Testing the Role of Reward and Punishment Sensitivity in Avoidance Behavior},
  author = {Sheynin, Jony and Moustafa, Ahmed A. and Beck, Kevin D. and Servatius, Richard J. and Myers, Catherine E.},
  year = {2015},
  month = apr,
  journal = {Behavioural Brain Research},
  volume = {283},
  pages = {121--138},
  issn = {01664328},
  doi = {10.1016/j.bbr.2015.01.033},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9EKD5UGS\\Sheynin et al. - 2015 - Testing the role of reward and punishment sensitiv.pdf}
}

@article{shiffman_2008,
  title = {Ecological {{Momentary Assessment}}},
  author = {Shiffman, Saul and Stone, Arthur A. and Hufford, Michael R.},
  year = {2008},
  month = apr,
  journal = {Annual Review of Clinical Psychology},
  volume = {4},
  number = {1},
  pages = {1--32},
  issn = {1548-5943, 1548-5951},
  doi = {10.1146/annurev.clinpsy.3.022806.091415},
  abstract = {Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Z2HZBETD\\Shiffman et al. - 2008 - Ecological Momentary Assessment.pdf}
}

@article{shmueli_2010,
  title = {To {{Explain}} or to {{Predict}}?},
  author = {Shmueli, Galit},
  year = {2010},
  month = aug,
  journal = {Statistical Science},
  volume = {25},
  number = {3},
  issn = {0883-4237},
  doi = {10.1214/10-STS330},
  abstract = {Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Y877JERC\\Shmueli - 2010 - To Explain or to Predict.pdf}
}

@article{shulman_1997,
  title = {Common {{Blood Flow Changes}} across {{Visual Tasks}}: {{I}}. {{Increases}} in {{Subcortical Structures}} and {{Cerebellum}} but {{Not}} in {{Nonvisual Cortex}}},
  shorttitle = {Common {{Blood Flow Changes}} across {{Visual Tasks}}},
  author = {Shulman, Gordon L. and Corbetta, Maurizio and Buckner, Randy L. and Fiez, Julie A. and Miezin, Francis M. and Raichle, Marcus E. and Petersen, Steven E.},
  year = {1997},
  month = oct,
  journal = {Journal of Cognitive Neuroscience},
  volume = {9},
  number = {5},
  pages = {624--647},
  issn = {0898-929X, 1530-8898},
  doi = {10.1162/jocn.1997.9.5.624},
  abstract = {Nine positron emission tomography (PET) studies of human visual information processing were reanalyzed to determine the consistency across experiments of blood flow increases during active tasks relative to passive viewing of the same stimulus array. N o consistent blood flow increases were found in cerebral cortex outside of the visual system, but increases were seen in the thalamus and cerebellum.Although most tasks involve increases in arousal,establishing an intention or behavioral goal, setting up control structures for sequencing task operations,detecting targets, etc.,these operations do not produce blood flow increases, detectable with the present methods, in localized cortical regions that are common across tasks. Common subcortical regions, however, may be involved.},
  langid = {english},
  annotation = {00152},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WUG878HL\\Shulman et al. - 1997 - Common Blood Flow Changes across Visual Tasks I. .pdf}
}

@article{siegmund_1995,
  title = {Testing for a Signal with Unknown Location and Scale in a Stationary {{Gaussian}} Random Field},
  author = {Siegmund, David O. and Worsley, Keith J.},
  year = {1995},
  journal = {The Annals of Statistics},
  pages = {608--639},
  annotation = {00183},
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}

@article{Siegmund1995,
  title = {Testing for a Signal with Unknown Location and Scale in a Stationary {{Gaussian}} Random Field},
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}

@article{silver_2016,
  title = {Mastering the Game of {{Go}} with Deep Neural Networks and Tree Search},
  author = {Silver, David and Huang, Aja and Maddison, Chris J. and Guez, Arthur and Sifre, Laurent and {van den Driessche}, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
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  journal = {Nature},
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  number = {7587},
  pages = {484--489},
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  doi = {10.1038/nature16961},
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}

@article{simen_2009,
  title = {Reward Rate Optimization in Two-Alternative Decision Making: {{Empirical}} Tests of Theoretical Predictions.},
  shorttitle = {Reward Rate Optimization in Two-Alternative Decision Making},
  author = {Simen, Patrick and Contreras, David and Buck, Cara and Hu, Peter and Holmes, Philip and Cohen, Jonathan D.},
  year = {2009},
  journal = {Journal of Experimental Psychology: Human Perception and Performance},
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  doi = {10.1037/a0016926},
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}

@article{simon_2011,
  title = {Neural {{Correlates}} of {{Forward Planning}} in a {{Spatial Decision Task}} in {{Humans}}},
  author = {Simon, D. A. and Daw, N. D.},
  year = {2011},
  journal = {Journal of Neuroscience},
  volume = {31},
  number = {14},
  pages = {5526--5539},
  issn = {0270-6474},
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  abstract = {Although reinforcement learning (RL) theories have been influential in characterizing the mechanisms for reward-guided choice in the brain, the predominant temporal difference (TD) algorithm cannot explain many flexible or goal-directed actions that have been demonstrated behaviorally. We investigate such actions by contrasting an RL algorithm that is model based, in that it relies on learning a map or model of the task and planning within it, to traditional model-free TD learning. To distinguish these approaches in humans, we used functional magnetic resonance imaging in a continuous spatial navigation task, in which frequent changes to the layout of the maze forced subjects continually to relearn their favored routes, thereby exposing the RL mechanisms used. We sought evidence for the neural substrates of such mechanisms by comparing choice behavior and blood oxygen level-dependent (BOLD) signals to decision variables extracted from simulations of either algorithm. Both choices and value-related BOLD signals in striatum, although most often associated with TD learning, were better explained by the model-based theory. Furthermore, predecessor quantities for the model-based value computation were correlated with BOLD signals in the medial temporal lobe and frontal cortex. These results point to a significant extension of both the computational and anatomical substrates for RL in the brain.},
  pmid = {21471389},
  annotation = {00135}
}

@article{sinz_,
  title = {Essential {{Mathematics}} for {{Neuroscience}}},
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}

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  title = {Einf\"uhrung in Die Imperative {{Programmierung}}},
  author = {Six, Hans-Werner},
  year = {2010},
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  address = {{Hagen}},
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@article{slotnick_2017,
  title = {Cluster Success: {{fMRI}} Inferences for Spatial Extent Have Acceptable False-Positive Rates},
  shorttitle = {Cluster Success},
  author = {Slotnick, Scott D.},
  year = {2017},
  month = jul,
  journal = {Cognitive Neuroscience},
  volume = {8},
  number = {3},
  pages = {150--155},
  issn = {1758-8928},
  doi = {10.1080/17588928.2017.1319350},
  abstract = {In an editorial (this issue), I argued that Eklund, Nichols, and Knutsson's `null data' reflected resting-state/default network activity that inflated their false-positive rates. Commentaries on that paper were received by Nichols, Eklund, and Knutsson (this issue), Hopfinger (this issue), and Cunningham and Koscik (this issue). In this author response, I consider these commentaries. Many issues stemming from Nichols et al. are identified including: (1) Nichols et al. did not provide convincing arguments that resting-state fMRI data reflect null data. (2) Eklund et al. presented one-sample t-test results in the main body of their paper showing that their permutation method was acceptable, while their supplementary results showed that this method produced false-positive rates that were similar to other methods. (3) Eklund et al. used the same event protocol for all the participants, which artifactually inflated the one-sample t-test false-positive rates. (4) At p {$<$} .001, using two-sample t-tests (which corrected for the flawed analysis), all the methods employed to correct for multiple comparisons had acceptable false-positive rates. (5) Eklund et al. contrasted resting-state periods, which produced many significant clusters of activity, while null data should arguably be associated with few, if any, significant activations. Eklund et al.'s entire set of results show that commonly employed methods to correct for multiple comparisons have acceptable false-positive rates. Following Hopfinger along with Cunningham and Koscik, it is also highlighted that rather than focusing on only type I error, type I error and type II error should be balanced in fMRI analysis.},
  pmid = {28403749},
  keywords = {clusterwise inference,default network,false cluster,false positive,familywise error,fMRI,multiple comparisons,type I error},
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}

@article{slutsky_1925,
  title = {\"Uber Stochastische {{Asymptoten}} Und {{Grenzwerte}}},
  author = {Slutsky, E},
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}

@unpublished{smith_2002,
  title = {A Tutorial on {{Principal Components Analysis}}},
  author = {Smith, Lindsay},
  year = {2002},
  address = {{Otago}},
  langid = {english},
  annotation = {02715  OCLC: 13580207},
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}

@article{smith_2004,
  title = {Advances in Functional and Structural {{MR}} Image Analysis and Implementation as {{FSL}}},
  author = {Smith, Stephen M. and Jenkinson, Mark and Woolrich, Mark W. and Beckmann, Christian F. and Behrens, Timothy E.J. and {Johansen-Berg}, Heidi and Bannister, Peter R. and De Luca, Marilena and Drobnjak, Ivana and Flitney, David E. and Niazy, Rami K. and Saunders, James and Vickers, John and Zhang, Yongyue and De Stefano, Nicola and Brady, J. Michael and Matthews, Paul M.},
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  month = jan,
  journal = {NeuroImage},
  volume = {23},
  pages = {S208-S219},
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  doi = {10.1016/j.neuroimage.2004.07.051},
  langid = {english},
  annotation = {07509},
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}

@article{smith_2009,
  title = {Threshold-Free Cluster Enhancement: {{Addressing}} Problems of Smoothing, Threshold Dependence and Localisation in Cluster Inference},
  shorttitle = {Threshold-Free Cluster Enhancement},
  author = {Smith, S and Nichols, T},
  year = {2009},
  month = jan,
  journal = {NeuroImage},
  volume = {44},
  number = {1},
  pages = {83--98},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2008.03.061},
  langid = {english},
  annotation = {02702},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SNUBD46E\\Smith and Nichols - 2009 - Threshold-free cluster enhancement Addressing pro.pdf}
}

@article{smith_2015,
  title = {Good Practice Principles for Sharing Individual Participant Data from Publicly Funded Clinical Trials},
  author = {Smith, Catrin Tudur and Hopkins, Carolyn and Sydes, Matt and Woolfall, Kerry and Clarke, Mike and Murray, Gordon and Williamson, Paula},
  year = {2015},
  month = dec,
  journal = {Trials},
  volume = {16},
  number = {S2},
  issn = {1745-6215},
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}

@article{smola_2004,
  title = {A Tutorial on Support Vector Regression},
  author = {Smola, Alex J. and Sch{\"o}lkopf, Bernhard},
  year = {2004},
  month = aug,
  journal = {Statistics and Computing},
  volume = {14},
  number = {3},
  pages = {199--222},
  issn = {0960-3174},
  doi = {10.1023/B:STCO.0000035301.49549.88},
  abstract = {In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.},
  langid = {english},
  annotation = {08452},
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}

@article{sokolov_2018,
  title = {Structural and Effective Brain Connectivity Underlying Biological Motion Detection},
  author = {Sokolov, Arseny A. and Zeidman, Peter and Erb, Michael and Ryvlin, Philippe and Friston, Karl J. and Pavlova, Marina A.},
  year = {2018},
  month = dec,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {115},
  number = {51},
  pages = {E12034-E12042},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1812859115},
  langid = {english},
  annotation = {00000},
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}

@article{soltani_2016,
  title = {Neural Substrates of Cognitive Biases during Probabilistic Inference},
  author = {Soltani, Alireza and Khorsand, Peyman and Guo, Clara and Farashahi, Shiva and Liu, Janet},
  year = {2016},
  month = apr,
  journal = {Nature Communications},
  volume = {7},
  pages = {11393},
  issn = {2041-1723},
  doi = {10.1038/ncomms11393},
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}

@article{solway_2012,
  ids = {solway_goal-directed_2012-1},
  title = {Goal-Directed Decision Making as Probabilistic Inference: {{A}} Computational Framework and Potential Neural Correlates.},
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  number = {1},
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  langid = {english},
  pmid = {22229491},
  keywords = {Decision making,Neuroeconomics,Planning,Probabilistic inference,Reward},
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}

@article{solway_2015,
  title = {Evidence Integration in Model-Based Tree Search},
  author = {Solway, Alec and Botvinick, Matthew M.},
  year = {2015},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {112},
  number = {37},
  pages = {11708--11713},
  issn = {0027-8424},
  doi = {10.1073/pnas.1505483112},
  abstract = {Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.},
  pmid = {26324932},
  annotation = {00021}
}

@article{song_2015,
  title = {{{DeID}} \textendash{} a Data Sharing Tool for Neuroimaging Studies},
  author = {Song, Xuebo and Wang, James and Wang, Anlin and Meng, Qingping and Prescott, Christian and Tsu, Loretta and Eckert, Mark A.},
  year = {2015},
  month = sep,
  journal = {Frontiers in Neuroscience},
  volume = {9},
  issn = {1662-453X},
  doi = {10.3389/fnins.2015.00325},
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}

@techreport{song_2018,
  type = {Preprint},
  title = {Sources of Suboptimality in a Minimalistic Explore-Exploit Task},
  author = {Song, Mingyu and Bnaya, Zahy and Ma, Wei Ji},
  year = {2018},
  month = jun,
  institution = {{Neuroscience}},
  doi = {10.1101/348474},
  abstract = {Balancing exploration and exploitation is a fundamental aspect of decision-making. It remains unknown whether people are close to optimal in striking this balance, and if not, how exactly their behavior deviates from optimality. Many existing paradigms are not ideally suited to answer this question, as they contain complexities such as non-stationary environments, stochasticity under exploitation, and reward distributions that are unknown to participants. Here, we introduce a task without such complexities, in which the optimal policy is to start off exploring and to switch to exploitation at most once in each sequence of decisions. The behavior of 49 laboratory and 143 online participants deviated both qualitatively and quantitatively from the optimal policy, even when allowing for bias and decision noise. Instead, people seem to follow a suboptimal rule in which they switch from exploration to exploitation when the highest reward so far exceeds a certain threshold. Moreover, we show that this threshold decreases approximately linearly with the proportion of the sequence that remains, suggesting a novel temporal ratio law. Finally, we find evidence for "sequence-level" variability which is shared across all decisions in the same sequence. Our results provide a new perspective on the explore-exploit dilemma, and emphasize the importance of examining sequence-level strategies and their variability when studying sequential decision-making.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2T2ULITK\\Song et al. - 2018 - Sources of suboptimality in a minimalistic explore.pdf}
}

@article{speekenbrink_2015,
  ids = {speekenbrink2015},
  title = {Uncertainty and {{Exploration}} in a {{Restless Bandit Problem}}},
  author = {Speekenbrink, Maarten and Konstantinidis, Emmanouil},
  year = {2015},
  month = apr,
  journal = {Topics in Cognitive Science},
  volume = {7},
  number = {2},
  pages = {351--367},
  publisher = {{Wiley Online Library}},
  issn = {17568757},
  doi = {10.1111/tops.12145},
  abstract = {Decision making in noisy and changing environments requires a fine balance between exploiting knowledge about good courses of action and exploring the environment in order to improve upon this knowledge. We present an experiment on a restless bandit task in which participants made repeated choices between options for which the average rewards changed over time. Comparing a number of computational models of participants' behavior in this task, we find evidence that a substantial number of them balanced exploration and exploitation by considering the probability that an option offers the maximum reward out of all the available options.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\Y8NYZP7F\\Speekenbrink und Konstantinidis - 2015 - Uncertainty and Exploration in a Restless Bandit P.pdf}
}

@article{spellman_2015,
  title = {A {{Short}} ({{Personal}}) {{Future History}} of {{Revolution}} 2.0},
  author = {Spellman, Barbara A.},
  year = {2015},
  month = nov,
  journal = {Perspectives on Psychological Science},
  volume = {10},
  number = {6},
  pages = {886--899},
  issn = {1745-6916, 1745-6924},
  doi = {10.1177/1745691615609918},
  abstract = {Crisis of replicability is one term that psychological scientists use for the current introspective phase we are in\textemdash I argue instead that we are going through a revolution analogous to a political revolution. Revolution 2.0 is an uprising focused on how we should be doing science now (i.e., in a 2.0 world). The precipitating events of the revolution have already been well-documented: failures to replicate, questionable research practices, fraud, etc. And the fact that none of these events is new to our field has also been well-documented. I suggest four interconnected reasons as to why this time is different: changing technology, changing demographics of researchers, limited resources, and misaligned incentives. I then describe two reasons why the revolution is more likely to catch on this time: technology (as part of the solution) and the fact that these concerns cut across social and life sciences\textemdash that is, we are not alone. Neither side in the revolution has behaved well, and each has characterized the other in extreme terms (although, of course, each has had a few extreme actors). Some suggested reforms are already taking hold (e.g., journals asking for more transparency in methods and analysis decisions; journals publishing replications) but the feared tyrannical requirements have, of course, not taken root (e.g., few journals require open data; there is no ban on exploratory analyses). Still, we have not yet made needed advances in the ways in which we accumulate, connect, and extract conclusions from our aggregated research. However, we are now ready to move forward by adopting incremental changes and by acknowledging the multiplicity of goals within psychological science.},
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  annotation = {00146},
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}

@article{spiegler_2013,
  title = {Systematic Approximations of Neural Fields through Networks of Neural Masses in the Virtual Brain},
  author = {Spiegler, A. and Jirsa, V.},
  year = {2013},
  month = dec,
  journal = {NeuroImage},
  volume = {83},
  pages = {704--725},
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}

@book{spivak_2008,
  title = {Calculus},
  author = {Spivak, Michael},
  year = {2008},
  edition = {Fourth},
  publisher = {{Publish or Perish, Inc}},
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}

@article{spreng_2012,
  title = {The {{Fallacy}} of a ``{{Task-Negative}}'' {{Network}}},
  author = {Spreng, R. Nathan},
  year = {2012},
  journal = {Frontiers in Psychology},
  volume = {3},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2012.00145},
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}

@article{srinivas_2012,
  title = {Gaussian {{Process Optimization}} in the {{Bandit Setting}}: {{No Regret}} and {{Experimental Design}}},
  shorttitle = {Gaussian {{Process Optimization}} in the {{Bandit Setting}}},
  author = {Srinivas, Niranjan and Krause, Andreas and Kakade, Sham M. and Seeger, Matthias},
  year = {2012},
  month = may,
  journal = {IEEE Transactions on Information Theory},
  volume = {58},
  number = {5},
  eprint = {0912.3995},
  eprinttype = {arxiv},
  pages = {3250--3265},
  issn = {0018-9448, 1557-9654},
  doi = {10.1109/TIT.2011.2182033},
  abstract = {Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve the important open problem of deriving regret bounds for this setting, which imply novel convergence rates for GP optimization. We analyze GP-UCB, an intuitive upper-confidence based algorithm, and bound its cumulative regret in terms of maximal information gain, establishing a novel connection between GP optimization and experimental design. Moreover, by bounding the latter in terms of operator spectra, we obtain explicit sublinear regret bounds for many commonly used covariance functions. In some important cases, our bounds have surprisingly weak dependence on the dimensionality. In our experiments on real sensor data, GP-UCB compares favorably with other heuristical GP optimization approaches.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning},
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}

@article{starke_2017,
  title = {Variational {{Bayesian Parameter Estimation Techniques}} for the {{General Linear Model}}},
  author = {Starke, Ludger and Ostwald, Dirk},
  year = {2017},
  month = sep,
  journal = {Frontiers in Neuroscience},
  volume = {11},
  issn = {1662-453X},
  doi = {10.3389/fnins.2017.00504},
  abstract = {Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation.},
  annotation = {00000},
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}

@article{starkweather_2017,
  ids = {starkweather_dopamine_2017-1},
  title = {Dopamine Reward Prediction Errors Reflect Hidden-State Inference across Time},
  author = {Starkweather, Clara Kwon and Babayan, Benedicte M and Uchida, Naoshige and Gershman, Samuel J},
  year = {2017},
  month = apr,
  journal = {Nature Neuroscience},
  volume = {20},
  number = {4},
  pages = {581--589},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.4520},
  langid = {english},
  pmid = {28263301},
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}

@article{steel_2019,
  title = {Beyond {{Calculations}}: {{A Course}} in {{Statistical Thinking}}},
  shorttitle = {Beyond {{Calculations}}},
  author = {Steel, E. Ashley and Liermann, Martin and Guttorp, Peter},
  year = {2019},
  month = mar,
  journal = {The American Statistician},
  volume = {73},
  number = {sup1},
  pages = {392--401},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2018.1505657},
  abstract = {Statisticians are in general agreement that there are flaws in how science is currently practiced; there is less agreement in how to make repairs. Our prescription for a Post-p {$<$} 0.05 Era is to develop and teach courses that expand our view of what constitutes the domain of statistics and thereby bridge undergraduate statistics coursework and the graduate student experience of applying statistics in research. Such courses can speed up the process of gaining statistical wisdom by giving students insight into the human propensity to make statistical errors, the meaning of a single test within a research project, ways in which p-values work and don't work as expected, the role of statistics in the lifecycle of science, and best practices for statistical communication. The course we have developed follows the story of how we use data to understand the world, leveraging simulation-based approaches to perform customized analyses and evaluate the behavior of statistical procedures. We provide ideas for expanding beyond the traditional classroom, two example activities, and a course syllabus as well as the set of statistical best practices for creating and consuming scientific information that we develop during the course.},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\M3H5IUEU\\Steel et al. - 2019 - Beyond Calculations A Course in Statistical Think.pdf}
}

@article{stefanics_2018,
  title = {Visual Mismatch and Predictive Coding: {{A}} Computational Single-Trial {{ERP}} Study},
  shorttitle = {Visual Mismatch and Predictive Coding},
  author = {Stefanics, Gabor and Heinzle, Jakob and Horv{\'a}th, Andr{\'a}s Attila and Stephan, Klaas Enno},
  year = {2018},
  month = mar,
  journal = {Journal of Neuroscience},
  pages = {3365--17},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3365-17.2018},
  abstract = {Predictive coding (PC) posits that the brain employs a generative model to infer the environmental causes of its sensory data and uses precision-weighted prediction errors (pwPE) to continuously update this model. While supported by much circumstantial evidence, experimental tests grounded in formal trial-by-trial predictions are rare. One partial exception are event-related potential (ERP) studies of the auditory mismatch negativity (MMN), where computational models have found signatures of pwPEs and related model-updating processes. Here, we tested this hypothesis in the visual domain, examining possible links between visual mismatch responses and pwPEs. We used a novel visual `roving standard' paradigm to elicit mismatch responses in humans (of both sexes) by unexpected changes in either color or emotional expression of faces. Using a hierarchical Bayesian model, we simulated pwPE trajectories of a Bayes-optimal observer and used these to conduct a comprehensive trial-by-trial analysis across the time\texttimes sensor space. We found significant modulation of brain activity by both color and emotion pwPEs. The scalp distribution and timing of these single-trial pwPE responses were in agreement with visual mismatch responses obtained by traditional averaging and subtraction (deviant-minus-standard) approaches. Finally, we compared the Bayesian model to a more classical change detection (CD) model of MMN. Model comparison revealed that trial-wise pwPEs explained the observed mismatch responses better than categorical change detection. Our results suggest that visual mismatch responses reflect trial-wise pwPEs, as postulated by PC. These findings go beyond classical ERP analyses of visual mismatch and illustrate the utility of computational analyses for studying automatic perceptual processes. SIGNIFICANCE STATEMENT Human perception is thought to rely on a predictive model of the environment which is updated via precision-weighted prediction errors (pwPE) when events violate expectations. This ``predictive coding'' view is supported by studies of the auditory mismatch negativity brain potential. However, it is less well known whether visual perception of mismatch relies on similar processes. Here we combined computational modeling and electroencephalography to test whether visual mismatch responses reflected trial-by-trial pwPEs. Applying a Bayesian model to series of face stimuli that violated expectations about color or emotional expression, we found significant modulation of brain activity by both color and emotion pwPEs. A categorical change detection model performed less convincingly. Our findings support the predictive coding interpretation of visual mismatch responses.},
  copyright = {Copyright \textcopyright{} 2018 the authors},
  langid = {english},
  pmid = {29581379},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\V5NLCSG2\\Stefanics et al_2018_Journal of Neuroscience_Visual mismatch and predictive coding.pdf;C\:\\Users\\dirko\\Zotero\\storage\\B32S972M\\JNEUROSCI.3365-17.html}
}

@article{stein_,
  title = {{Analyse von Paneldaten - Ein Abriss ausgew\"ahlter Verfahren}},
  author = {Stein, Petra and Bekalarczyk, Dawid},
  pages = {135},
  langid = {german},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BAM49AMQ\\Stein and Bekalarczyk - Analyse von Paneldaten - Ein Abriss ausgewählter V.pdf}
}

@book{stein_2005,
  title = {Real Analysis: Measure Theory, Integration, and {{Hilbert}} Spaces},
  shorttitle = {Real Analysis},
  author = {Stein, Elias M. and Shakarchi, Rami},
  year = {2005},
  series = {Princeton Lectures in Analysis},
  number = {v. 3},
  publisher = {{Princeton University Press}},
  address = {{Princeton, N.J}},
  isbn = {978-0-691-11386-9},
  langid = {english},
  lccn = {QA320 .S84 2005},
  keywords = {Functional analysis,Hilbertruimten,Integrals; Generalized,Maattheorie,Measure theory},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\92LN9JSR\\Stein and Shakarchi - 2005 - Real analysis measure theory, integration, and Hi.pdf;C\:\\Users\\dirko\\Zotero\\storage\\WWQ88MP2\\Stein and Shakarchi - 2005 - Real analysis measure theory, integration, and Hi.pdf}
}

@book{stein_2013,
  title = {Interpolation of Spatial Data: Some Theory for Kriging.},
  shorttitle = {Interpolation of Spatial Data},
  author = {Stein, Michael L},
  year = {2013},
  publisher = {{Springer}},
  address = {{Place of publication not identified}},
  isbn = {978-1-4612-7166-6},
  langid = {english},
  annotation = {02939  OCLC: 830022093},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7T5DUS2Q\\Stein_2013_Interpolation of spatial data.pdf}
}

@article{steinke_2017,
  title = {The {{School Malaise Trap Program}}: {{Coupling}} Educational Outreach with Scientific Discovery},
  shorttitle = {The {{School Malaise Trap Program}}},
  author = {Steinke, Dirk and Breton, Vanessa and Berzitis, Emily and Hebert, Paul D. N.},
  year = {2017},
  month = apr,
  journal = {PLOS Biology},
  volume = {15},
  number = {4},
  pages = {e2001829},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.2001829},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3ZV9W3LT\\Steinke et al. - 2017 - The School Malaise Trap Program Coupling educatio.pdf;C\:\\Users\\dirko\\Zotero\\storage\\DQ5Z4MRT\\Steinke_PB_17.pdf;C\:\\Users\\dirko\\Zotero\\storage\\LJL7IUD3\\Steinke et al. - 2017 - The School Malaise Trap Program Coupling educatio.pdf}
}

@article{stephan_2007,
  title = {Comparing Hemodynamic Models with {{DCM}}},
  author = {Stephan, Klaas Enno and Weiskopf, Nikolaus and Drysdale, Peter M. and Robinson, Peter A. and Friston, Karl J.},
  year = {2007},
  month = nov,
  journal = {NeuroImage},
  volume = {38},
  number = {3},
  pages = {387--401},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2007.07.040},
  langid = {english},
  annotation = {00319},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DPYZ2WY9\\Stephan et al. - 2007 - Comparing hemodynamic models with DCM.pdf}
}

@article{stephan_2009,
  ids = {stephan2009},
  title = {Bayesian Model Selection for Group Studies},
  author = {Stephan, Klaas Enno and Penny, Will D. and Daunizeau, Jean and Moran, Rosalyn J. and Friston, Karl J.},
  year = {2009},
  month = jul,
  journal = {NeuroImage},
  volume = {46},
  number = {4},
  pages = {1004--1017},
  publisher = {{Elsevier}},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.03.025},
  abstract = {Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to intersubject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers. We expect that this new random effects method will prove useful for a wide range of group studies, not only in the context of DCM, but also for other modelling endeavours, e.g. comparing different source reconstruction methods for EEG/MEG or selecting among competing computational models of learning and decision-making.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KKM6ABFB\\Stephan et al. - 2009 - Bayesian model selection for group studies.pdf}
}

@article{stephan_2015,
  ids = {stephan_translational_2015},
  title = {Translational {{Perspectives}} for {{Computational Neuroimaging}}},
  author = {Stephan, Klaas~E. and Iglesias, Sandra and Heinzle, Jakob and Diaconescu, Andreea~O.},
  year = {2015},
  month = aug,
  journal = {Neuron},
  volume = {87},
  number = {4},
  pages = {716--732},
  issn = {08966273},
  doi = {10.1016/j.neuron.2015.07.008},
  langid = {english},
  annotation = {00090},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8GNIHZS2\\Stephan et al. - 2015 - Translational Perspectives for Computational Neuro.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PXWB53DC\\Stephan et al. - 2015 - Translational Perspectives for Computational Neuro.pdf}
}

@article{stephan_2017,
  ids = {stephan_computational_2017-1},
  title = {Computational Neuroimaging Strategies for Single Patient Predictions},
  author = {Stephan, K.E. and Schlagenhauf, F. and Huys, Q.J.M. and Raman, S. and Aponte, E.A. and Brodersen, K.H. and Rigoux, L. and Moran, R.J. and Daunizeau, J. and Dolan, R.J. and Friston, K.J. and Heinz, A.},
  year = {2017},
  month = jan,
  journal = {NeuroImage},
  volume = {145},
  pages = {180--199},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2016.06.038},
  abstract = {Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference; focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches \textendash Bayesian model selection and generative embedding \textendash{} which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, modelbased approach and the statistical perspectives afforded by machine learning.},
  langid = {english},
  annotation = {00065},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QHK9XQBU\\Stephan et al. - 2017 - Computational neuroimaging strategies for single p.pdf;C\:\\Users\\dirko\\Zotero\\storage\\W72JFD53\\Stephan et al. - 2017 - Computational neuroimaging strategies for single p.pdf}
}

@article{stephan_2017a,
  title = {Laminar {{fMRI}} and Computational Theories of Brain Function},
  author = {Stephan, K.E. and Petzschner, F.H. and Kasper, L. and Bayer, J. and Wellstein, K.V. and Stefanics, G. and Pruessmann, K.P. and Heinzle, J.},
  year = {2017},
  month = nov,
  journal = {NeuroImage},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2017.11.001},
  abstract = {Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans.},
  langid = {english},
  annotation = {00002},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HAHWF3S9\\Stephan et al. - 2017 - Laminar fMRI and computational theories of brain f.pdf}
}

@article{stephend.casey_1994,
  title = {Systems of {{Convolution Equations}}, {{Deconvolution}}, {{Shannon Sampling}}, and the {{Wavelet}} and {{Gabor Transforms}}},
  author = {Stephen D. Casey and David F. Walnut},
  year = {1994},
  journal = {SIAM Review},
  volume = {36},
  number = {4,},
  pages = {537--577},
  annotation = {00065},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CJ5P34U2\\Stephen D. Casey and David F. Walnut - 1994 - Systems of Convolution Equations, Deconvolution, S.pdf;C\:\\Users\\dirko\\Zotero\\storage\\HAHPZZF5\\Stephen D. Casey and David F. Walnut - 1994 - Systems of Convolution Equations, Deconvolution, S.pdf}
}

@article{sterling_1959,
  title = {Publication {{Decisions}} and {{Their Possible Effects}} on {{Inferences Drawn}} from {{Tests}} of {{Significance--Or Vice Versa}}},
  author = {Sterling, Theodore D.},
  year = {1959},
  month = mar,
  journal = {Journal of the American Statistical Association},
  volume = {54},
  number = {285},
  pages = {30},
  issn = {01621459},
  doi = {10.2307/2282137},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2NP6DBLK\\Sterling_JAS_59.pdf}
}

@article{stevenl.scott_2002,
  title = {Bayesian {{Methods}} for {{Hidden Markov Models}}: {{Recursive Computing}} in the 21st {{Century}}},
  author = {Steven L. Scott},
  year = {2002},
  journal = {Journal of the American Statistical Association},
  volume = {97},
  number = {457},
  langid = {english},
  annotation = {00505},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WB33SKRX\\Steven L. Scott - 2002 - Bayesian Methods for Hidden Markov Models Recursi.pdf}
}

@article{stevens_1946,
  title = {On the {{Theory}} of {{Scales}} of {{Measurement}}},
  author = {Stevens, S. S.},
  year = {1946},
  journal = {Science, New Series},
  volume = {103},
  number = {2684},
  pages = {677--680},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XMFU3GRU\\Stevens - 1946 - On the Theory of Scales of Measurement.pdf}
}

@article{stevens_2015,
  title = {The {{Proposed Data Protection Regulation}} and {{Its Potential Impact}} on {{Social Sciences Research}} in the {{UK}}},
  author = {Stevens, L.},
  year = {2015},
  journal = {European Data Protection Law Review},
  volume = {1},
  number = {2},
  pages = {97--112},
  issn = {23642831, 2364284X},
  doi = {10.21552/EDPL/2015/2/4},
  langid = {english},
  annotation = {00003},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GWCZZJFH\\Stevens - 2015 - The Proposed Data Protection Regulation and Its Po.pdf;C\:\\Users\\dirko\\Zotero\\storage\\I4ATDQ6C\\Stevens - 2015 - The Proposed Data Protection Regulation and Its Po.pdf}
}

@book{stewart_2009,
  title = {Probability, {{Markov}} Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling},
  shorttitle = {Probability, {{Markov}} Chains, Queues, and Simulation},
  author = {Stewart, William J.},
  year = {2009},
  publisher = {{Princeton University Press}},
  address = {{Princeton, N.J}},
  isbn = {978-0-691-14062-9},
  langid = {english},
  lccn = {QA273 .S7532 2009},
  keywords = {Computer simulation,Markov processes,Probabilities,Queuing theory},
  annotation = {00003  OCLC: ocn255018592},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NDYWNVMN\\Stewart - 2009 - Probability, Markov chains, queues, and simulation.pdf}
}

@book{steyer_2001,
  title = {{Messen und Testen: mit \"Ubungen und L\"osungen}},
  shorttitle = {{Messen und Testen}},
  author = {Steyer, Rolf and Eid, Michael},
  year = {2001},
  series = {{Springer-Lehrbuch}},
  publisher = {{Springer}},
  address = {{Berlin}},
  isbn = {978-3-540-67919-6},
  langid = {german},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\E3ZMLIZC\\Rolf Steyer, Michael Eid - Messen und Testen_ Mit _bungen und L_sungen 2. Auflage-Springer (2000).pdf}
}

@article{steyvers_2009,
  ids = {steyversBayesianAnalysisHuman2009a},
  title = {A {{Bayesian}} Analysis of Human Decision-Making on Bandit Problems},
  author = {Steyvers, Mark and Lee, Michael D. and Wagenmakers, Eric-Jan},
  year = {2009},
  month = jun,
  journal = {Journal of Mathematical Psychology},
  volume = {53},
  number = {3},
  pages = {168--179},
  issn = {00222496},
  doi = {10.1016/j.jmp.2008.11.002},
  abstract = {The bandit problem is a dynamic decision-making task that is simply described, well-suited to controlled laboratory study, and representative of a broad class of real-world problems. In bandit problems, people must choose between a set of alternatives, each with different unknown reward rates, to maximize the total reward they receive over a fixed number of trials. A key feature of the task is that it challenges people to balance the exploration of unfamiliar choices with the exploitation of familiar ones. We use a Bayesian model of optimal decision-making on the task, in which how people balance exploration with exploitation depends on their assumptions about the distribution of reward rates. We also use Bayesian model selection measures that assess how well people adhere to an optimal decision process, compared to simpler heuristic decision strategies. Using these models, we make inferences about the decision-making of 451 participants who completed a set of bandit problems, and relate various measures of their performance to other psychological variables, including psychometric assessments of cognitive abilities and personality traits. We find clear evidence of individual differences in the way the participants made decisions on the bandit problems, and some interesting correlations with measures of general intelligence.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\47GGKZV9\\Steyvers et al. - 2009 - A Bayesian analysis of human decision-making on ba.pdf;C\:\\Users\\dirko\\Zotero\\storage\\F2R2I2PH\\Steyvers et al. - 2009 - A Bayesian analysis of human decision-making on ba.pdf}
}

@article{stodden_2018,
  ids = {stodden_empirical_2018a},
  title = {An Empirical Analysis of Journal Policy Effectiveness for Computational Reproducibility},
  author = {Stodden, Victoria and Seiler, Jennifer and Ma, Zhaokun},
  year = {2018},
  month = mar,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {115},
  number = {11},
  pages = {2584--2589},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1708290115},
  abstract = {A key component of scientific communication is sufficient information for other researchers in the field to reproduce published findings. For computational and data-enabled research, this has often been interpreted to mean making available the raw data from which results were generated, the computer code that generated the findings, and any additional information needed such as workflows and input parameters. Many journals are revising author guidelines to include data and code availability. This work evaluates the effectiveness of journal policy that requires the data and code necessary for reproducibility be made available postpublication by the authors upon request. We assess the effectiveness of such a policy by (i) requesting data and code from authors and (ii) attempting replication of the published findings. We chose a random sample of 204 scientific papers published in the journal Science after the implementation of their policy in February 2011. We found that we were able to obtain artifacts from 44\% of our sample and were able to reproduce the findings for 26\%. We find this policy\textemdash author remission of data and code postpublication upon request\textemdash an improvement over no policy, but currently insufficient for reproducibility.},
  copyright = {\textcopyright{} 2018 . Published under the PNAS license.},
  langid = {english},
  pmid = {29531050},
  keywords = {code access,data access,open science,reproducibility policy,reproducible research},
  annotation = {00000},
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}

@techreport{stojic_2018,
  type = {Preprint},
  title = {It's New, but Is It Good? {{How}} Generalization and Uncertainty Guide the Exploration of Novel Options},
  shorttitle = {It's New, but Is It Good?},
  author = {Stojic, Hrvoje and Schulz, Eric and Analytis, Pantelis P. and Speekenbrink, Maarten},
  year = {2018},
  month = nov,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/p6zev},
  abstract = {How do people decide whether to try out novel options as opposed to tried-and-tested ones? We argue that they infer a novel option's reward from contextual information learned from functional relations and take uncertainty into account when making a decision. We propose a Bayesian optimization model to describe their learning and decision making. This model relies on similarity-based learning of functional relationships between features and rewards, and a choice rule that balances exploration and exploitation by combining predicted rewards and the uncertainty of these predictions. Our model makes two main predictions. First, decision makers who learn functional relationships will generalize based on the learned reward function, choosing novel options only if their predicted reward is high. Second, they will take uncertainty about the function into account, and prefer novel options that can reduce this uncertainty. We test these predictions in three preregistered experiments in which we examine participants' preferences for novel options using a feature-based multi-armed bandit task in which rewards are a noisy function of observable features. Our results reveal strong evidence for functional exploration and moderate evidence for uncertainty-guided exploration. However, whether or not participants chose a novel option also depended on their attention, as well as reflecting on the value of the options. These results advance our understanding of people's reactions in the face of novelty.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UD6WSSGX\\Stojic et al. - 2018 - It’s new, but is it good How generalization and u.pdf}
}

@book{stone_1975,
  title = {Theory of Optimal Search},
  author = {Stone, Lawrence D.},
  year = {1975},
  series = {Mathematics in Science and Engineering},
  number = {v. 118},
  publisher = {{Academic Press}},
  address = {{New York}},
  isbn = {978-0-12-672450-9},
  langid = {english},
  lccn = {T57.97 .S76},
  keywords = {Search theory},
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}

@book{stone_2016,
  title = {Optimal {{Search}} for {{Moving Targets}}},
  author = {Stone, Lawrence D. and Royset, Johannes O. and Washburn, Alan R.},
  year = {2016},
  series = {International {{Series}} in {{Operations Research}} \& {{Management Science}}},
  volume = {237},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
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}

@article{storey_2002,
  title = {A Direct Approach to False Discovery Rates},
  author = {Storey, John D.},
  year = {2002},
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}

@book{strang_2009,
  title = {Introduction to {{Linear Algebra}}},
  shorttitle = {4th},
  author = {Strang, Gilbert},
  year = {2009},
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}

@article{strange_2008,
  title = {Authorship: Why Not Just Toss a Coin?},
  shorttitle = {Authorship},
  author = {Strange, Kevin},
  year = {2008},
  month = sep,
  journal = {American Journal of Physiology-Cell Physiology},
  volume = {295},
  number = {3},
  pages = {C567-C575},
  issn = {0363-6143, 1522-1563},
  doi = {10.1152/ajpcell.00208.2008},
  langid = {english},
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}

@article{strelioff_2007,
  title = {Inferring {{Markov}} Chains: {{Bayesian}} Estimation, Model Comparison, Entropy Rate, and out-of-Class Modeling},
  shorttitle = {Inferring {{Markov}} Chains},
  author = {Strelioff, Christopher C. and Crutchfield, James P. and H{\"u}bler, Alfred W.},
  year = {2007},
  month = jul,
  journal = {Physical Review E},
  volume = {76},
  number = {1},
  issn = {1539-3755, 1550-2376},
  doi = {10.1103/PhysRevE.76.011106},
  langid = {english},
  annotation = {00000},
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}

@article{student_1908,
  title = {The {{Probable Error}} of a {{Mean}}},
  author = {Student},
  year = {1908},
  journal = {Biometrika},
  volume = {6},
  number = {1},
  pages = {1--25},
  langid = {english},
  annotation = {01300},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FF6YH8L6\\Student - 1908 - The Probable Error of a Mean.pdf}
}

@article{sturges_1926,
  title = {The {{Choice}} of a {{Class Interval}}},
  author = {Sturges, Herbert A.},
  year = {1926},
  month = mar,
  journal = {Journal of the American Statistical Association},
  volume = {21},
  number = {153},
  pages = {65--66},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.1926.10502161},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FWJXQTT4\\Sturges - 1926 - The Choice of a Class Interval.pdf}
}

@article{styan_1973,
  title = {Hadamard Products and Multivariate Statistical Analysis},
  author = {Styan, George P.H.},
  year = {1973},
  journal = {Linear Algebra and its Applications},
  volume = {6},
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}

@incollection{suck_2015,
  title = {Measurement, {{Representational Theory}} Of},
  booktitle = {International {{Encyclopedia}} of the {{Social}} \& {{Behavioral Sciences}}},
  author = {Suck, Reinhard},
  year = {2015},
  pages = {856--861},
  publisher = {{Elsevier}},
  doi = {10.1016/B978-0-08-097086-8.43064-3},
  abstract = {The principles of representational measurement theory are expounded in historical and contemporary perspective, including a preliminary formulation of representation, uniqueness, and meaningfulness. The whole article is based on the concept of an ordered relational structure. The definition and representation theorems of extensive and difference structures are presented and discussed. These are ordered relational structures with operations or difference like relations defined on them. Stevens's classification of scale type (i.e., the classical ratio, interval, and ordinal scales) and its modern development in the context of the automorphism group of a measurement structure are dealt with, leading naturally to the problem of meaningfulness of numerical statements. Finally problems and applications of the representational theory of measurement are pointed out.},
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}

@incollection{suck_2015a,
  title = {Measurement, {{Representational Theory}} Of},
  booktitle = {International {{Encyclopedia}} of the {{Social}} \& {{Behavioral Sciences}}},
  author = {Suck, Reinhard},
  year = {2015},
  pages = {856--861},
  publisher = {{Elsevier}},
  doi = {10.1016/B978-0-08-097086-8.43064-3},
  abstract = {The principles of representational measurement theory are expounded in historical and contemporary perspective, including a preliminary formulation of representation, uniqueness, and meaningfulness. The whole article is based on the concept of an ordered relational structure. The definition and representation theorems of extensive and difference structures are presented and discussed. These are ordered relational structures with operations or difference like relations defined on them. Stevens's classification of scale type (i.e., the classical ratio, interval, and ordinal scales) and its modern development in the context of the automorphism group of a measurement structure are dealt with, leading naturally to the problem of meaningfulness of numerical statements. Finally problems and applications of the representational theory of measurement are pointed out.},
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}

@article{sumner_2014,
  title = {The Association between Exaggeration in Health Related Science News and Academic Press Releases: Retrospective Observational Study},
  shorttitle = {The Association between Exaggeration in Health Related Science News and Academic Press Releases},
  author = {Sumner, P. and {Vivian-Griffiths}, S. and Boivin, J. and Williams, A. and Venetis, C. A. and Davies, A. and Ogden, J. and Whelan, L. and Hughes, B. and Dalton, B. and Boy, F. and Chambers, C. D.},
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  volume = {349},
  number = {dec09 7},
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  issn = {1756-1833},
  doi = {10.1136/bmj.g7015},
  abstract = {Objective To identify the source (press releases or news) of distortions, exaggerations, or changes to the main conclusions drawn from research that could potentially influence a reader's health related behaviour.},
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@article{sun_,
  title = {On the {{Equivalence Between Canonical Correlation Analysis}} and {{Orthonormalized Partial Least Squares}}},
  author = {Sun, Liang},
  pages = {6},
  abstract = {Canonical correlation analysis (CCA) and partial least squares (PLS) are well-known techniques for feature extraction from two sets of multidimensional variables. The fundamental difference between CCA and PLS is that CCA maximizes the correlation while PLS maximizes the covariance. Although both CCA and PLS have been applied successfully in various applications, the intrinsic relationship between them remains unclear. In this paper, we attempt to address this issue by showing the equivalence relationship between CCA and orthonormalized partial least squares (OPLS), a variant of PLS. We further extend the equivalence relationship to the case when regularization is employed for both sets of variables. In addition, we show that the CCA projection for one set of variables is independent of the regularization on the other set of variables. We have performed experimental studies using both synthetic and real data sets and our results confirm the established equivalence relationship. The presented analysis provides novel insights into the connection between these two existing algorithms as well as the effect of the regularization.},
  langid = {english},
  annotation = {00061},
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}

@book{suppes_1957,
  title = {Introduction to Logic},
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}

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}

@book{sutton_2018,
  ids = {sutton2018},
  title = {Reinforcement Learning: An Introduction},
  shorttitle = {Reinforcement Learning},
  author = {Sutton, Richard S. and Barto, Andrew G.},
  year = {2018},
  series = {Adaptive Computation and Machine Learning Series},
  edition = {Second edition},
  publisher = {{The MIT Press}},
  address = {{Cambridge, Massachusetts}},
  abstract = {"Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."--},
  isbn = {978-0-262-03924-6},
  langid = {english},
  lccn = {Q325.6 .R45 2018},
  keywords = {Reinforcement learning},
  annotation = {30408},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QYXC2THT\\Sutton and Barto - 2018 - Reinforcement learning an introduction.pdf}
}

@article{svoronos_1980,
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  journal = {International Journal of Control},
  volume = {31},
  number = {1},
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  doi = {10.1080/00207178008961032},
  langid = {english},
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}

@article{szepesvari_2010,
  title = {Algorithms for {{Reinforcement Learning}}},
  author = {Szepesv{\'a}ri, Csaba},
  year = {2010},
  month = jan,
  journal = {Synthesis Lectures on Artificial Intelligence and Machine Learning},
  volume = {4},
  number = {1},
  pages = {1--103},
  issn = {1939-4608, 1939-4616},
  doi = {10.2200/S00268ED1V01Y201005AIM009},
  langid = {english},
  annotation = {00940},
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}

@article{szollosi_2019,
  title = {Is {{Preregistration Worthwhile}}?},
  author = {Szollosi, Aba and Kellen, David and Navarro, Danielle J. and Shiffrin, Richard and {van Rooij}, Iris and Van Zandt, Trisha and Donkin, Chris},
  year = {2019},
  month = dec,
  journal = {Trends in Cognitive Sciences},
  pages = {S1364661319302852},
  issn = {13646613},
  doi = {10.1016/j.tics.2019.11.009},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YKIKAXJK\\Szollosi et al. - 2019 - Is Preregistration Worthwhile.pdf}
}

@article{szucs_2016,
  ids = {szucs_empirical_2017},
  title = {Empirical Assessment of Published Effect Sizes and Power in the Recent Cognitive Neuroscience and Psychology Literature},
  author = {Szucs, Denes and Ioannidis, John PA},
  year = {2016},
  journal = {BioRxiv},
  pages = {071530},
  annotation = {00051},
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}

@article{talmi_2009,
  title = {How {{Humans Integrate}} the {{Prospects}} of {{Pain}} and {{Reward}} during {{Choice}}},
  author = {Talmi, D. and Dayan, P. and Kiebel, S. J. and Frith, C. D. and Dolan, R. J.},
  year = {2009},
  month = nov,
  journal = {Journal of Neuroscience},
  volume = {29},
  number = {46},
  pages = {14617--14626},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.2026-09.2009},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NQ8CQQDT\\Talmi et al. - 2009 - How Humans Integrate the Prospects of Pain and Rew.pdf}
}

@book{tango_2017,
  title = {Repeated {{Measures Design}} with {{Generalized Linear Mixed Models}} for {{Randomized Controlled Trials}}},
  author = {Tango, Toshiro},
  year = {2017},
  month = sep,
  edition = {Zeroth},
  publisher = {{Chapman and Hall/CRC}},
  doi = {10.1201/9781315152097},
  isbn = {978-1-4987-4790-5},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DG5SHT4Z\\Tango - 2017 - Repeated Measures Design with Generalized Linear M.pdf}
}

@article{tanner_2015,
  title = {How Inappropriate High-Pass Filters Can Produce Artifactual Effects and Incorrect Conclusions in {{ERP}} Studies of Language and Cognition: {{High-pass}} Filtering and Artifactual {{ERP}} Effects},
  shorttitle = {How Inappropriate High-Pass Filters Can Produce Artifactual Effects and Incorrect Conclusions in {{ERP}} Studies of Language and Cognition},
  author = {Tanner, Darren and {Morgan-Short}, Kara and Luck, Steven J.},
  year = {2015},
  month = aug,
  journal = {Psychophysiology},
  volume = {52},
  number = {8},
  pages = {997--1009},
  issn = {00485772},
  doi = {10.1111/psyp.12437},
  abstract = {Although it is widely known that high-pass filters can reduce the amplitude of slow ERP components, these filters can also introduce artifactual peaks that lead to incorrect conclusions. To demonstrate this and provide evidence about optimal filter settings, we recorded ERPs in a typical language processing paradigm involving syntactic and semantic violations. Unfiltered results showed standard N400 and P600 effects in the semantic and syntactic violation conditions, respectively. However, high-pass filters with cutoffs at 0.3 Hz and above produced artifactual effects of opposite polarity before the true effect. That is, excessive high-pass filtering introduced a significant N400 effect preceding the P600 in the syntactic condition, and a significant P2 effect preceding the N400 in the semantic condition. Thus, inappropriate use of high-pass filters can lead to false conclusions about which components are influenced by a given manipulation. The present results also lead to practical recommendations for high-pass filter settings that maximize statistical power while minimizing filtering artifacts.},
  langid = {english},
  annotation = {00000},
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}

@article{tarhonen_,
  title = {{{PSEUDONYMISATION OF PERSONAL DATA ACCORDING TO THE GENERAL DATA PROTECTION REGULATION}}},
  author = {Tarhonen, Laura},
  pages = {23},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DKVZ2W26\\Tarhonen - PSEUDONYMISATION OF PERSONAL DATA ACCORDING TO THE.pdf;C\:\\Users\\dirko\\Zotero\\storage\\HS7HFJCA\\Tarhonen - PSEUDONYMISATION OF PERSONAL DATA ACCORDING TO THE.pdf}
}

@techreport{taylor_,
  title = {Notes on Random Fields},
  author = {Taylor, Jonathan},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\M8RHLX6W\\Taylor_Notes on random fields.pdf}
}

@article{taylor_2005,
  title = {Validity of the Expected {{Euler}} Characteristic Heuristic},
  author = {Taylor, Jonathan and Takemura, Akimichi and Adler, Robert J.},
  year = {2005},
  month = jul,
  journal = {The Annals of Probability},
  volume = {33},
  number = {4},
  pages = {1362--1396},
  issn = {0091-1798, 2168-894X},
  abstract = {We study the accuracy of the expected Euler characteristic approximation to the distribution of the maximum of a smooth, centered, unit variance Gaussian process f. Using a point process representation of the error, valid for arbitrary smooth processes, we show that the error is in general exponentially smaller than any of the terms in the approximation. We also give a lower bound on this exponential rate of decay in terms of the maximal variance of a family of Gaussian processes fx, derived from the original process f.},
  langid = {english},
  mrnumber = {MR2150192},
  zmnumber = {1083.60031},
  keywords = {Euler characteristic,excursions,Gaussian processes,manifolds,point processes,Random fields,volume of tubes},
  annotation = {00081},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\DURXX99U\\Taylor et al_2005_The Annals of Probability_Validity of the expected Euler characteristic heuristic.pdf;C\:\\Users\\dirko\\Zotero\\storage\\ZG9ZKZN8\\1120224584.html}
}

@article{taylor_2007,
  title = {Detecting {{Sparse Signals}} in {{Random Fields}}, {{With}} an {{Application}} to {{Brain Mapping}}},
  author = {Taylor, Jonathan E and Worsley, Keith J},
  year = {2007},
  month = sep,
  journal = {Journal of the American Statistical Association},
  volume = {102},
  number = {479},
  pages = {913--928},
  issn = {0162-1459, 1537-274X},
  doi = {10.1198/016214507000000815},
  langid = {english},
  annotation = {00088},
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}

@article{taylor_2008,
  title = {Random Fields of Multivariate Test Statistics, with Applications to Shape Analysis},
  author = {Taylor, J. E. and Worsley, K. J.},
  year = {2008},
  month = feb,
  journal = {The Annals of Statistics},
  volume = {36},
  number = {1},
  pages = {1--27},
  issn = {0090-5364},
  doi = {10.1214/009053607000000406},
  langid = {english},
  annotation = {00062},
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}

@article{taylor_2009,
  title = {A {{Tribute}} to: {{Keith Worsley}} \textemdash{} 1951\textendash 2009},
  shorttitle = {A {{Tribute}} To},
  author = {Taylor, Jonathan and Evans, Alan and Friston, Karl},
  year = {2009},
  month = jul,
  journal = {NeuroImage},
  volume = {46},
  number = {4},
  pages = {891--894},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2009.04.026},
  langid = {english},
  annotation = {00002},
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}

@article{tegmark_2016,
  title = {Improved Measures of Integrated Information},
  author = {Tegmark, Max},
  year = {2016},
  journal = {PLoS computational biology},
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}

@article{tenopir_2011,
  title = {Data {{Sharing}} by {{Scientists}}: {{Practices}} and {{Perceptions}}},
  shorttitle = {Data {{Sharing}} by {{Scientists}}},
  author = {Tenopir, Carol and Allard, Suzie and Douglass, Kimberly and Aydinoglu, Arsev Umur and Wu, Lei and Read, Eleanor and Manoff, Maribeth and Frame, Mike},
  editor = {Neylon, Cameron},
  year = {2011},
  month = jun,
  journal = {PLoS ONE},
  volume = {6},
  number = {6},
  pages = {e21101},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0021101},
  abstract = {Background: Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers \textendash{} data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results. Methodology/Principal Findings: A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the shortand long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region. Conclusions/Significance: Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.},
  langid = {english},
  annotation = {00640},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2HP47BNB\\Tenopir et al. - 2011 - Data Sharing by Scientists Practices and Percepti.PDF}
}

@article{tenopir_2015,
  title = {Changes in {{Data Sharing}} and {{Data Reuse Practices}} and {{Perceptions}} among {{Scientists Worldwide}}},
  author = {Tenopir, Carol and Dalton, Elizabeth D. and Allard, Suzie and Frame, Mike and Pjesivac, Ivanka and Birch, Ben and Pollock, Danielle and Dorsett, Kristina},
  editor = {{van den Besselaar}, Peter},
  year = {2015},
  month = aug,
  journal = {PLOS ONE},
  volume = {10},
  number = {8},
  pages = {e0134826},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0134826},
  abstract = {The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3\textendash 4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.},
  langid = {english},
  annotation = {00085},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WP5TRLGW\\Tenopir et al. - 2015 - Changes in Data Sharing and Data Reuse Practices a.PDF}
}

@article{thanoon_,
  title = {Factor {{Analysis Model}}},
  author = {Thanoon, Thanoon Y},
  pages = {15},
  abstract = {Expectation maximization algorithm (EM) is used to create estimator with the same qualities of maximum likelihood estimator by taking into consideration the existence of two types of data, data viewing (observe data) and hidden data (missing data). Maximum likelihood method in factor analysis is applied in this research to estimate the parameters of the selected model using expectation maximization algorithm. This method is applied on breast cancer data. The results shown that most of variables are important in this study except first variable (level of education) and fifth variable (use of hormone treatment).},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\F4GGNXUR\\Thanoon - Factor Analysis Model.pdf}
}

@article{thayer_1986,
  title = {Activation-{{Deactivation Adjective Check List}}: {{Current Overview}} and {{Structural Analysis}}},
  shorttitle = {Activation-{{Deactivation Adjective Check List}}},
  author = {Thayer, Robert E.},
  year = {1986},
  month = apr,
  journal = {Psychological Reports},
  volume = {58},
  number = {2},
  pages = {607--614},
  issn = {0033-2941, 1558-691X},
  doi = {10.2466/pr0.1986.58.2.607},
  abstract = {The Activation-Deactivation Adjective Check List (AD ACL) is a multidimensional self-rating test constructed and extensively validated for rapid assessments of momentary activation or arousal states. The two core dimensions, energetic arousal (including tiredness) and tense arousal (including calmness) have been replicated repeatedly. They bear a complex relationship to each other involving positive and negative correlations at different levels of intensity. The dimensions are associated with a variety of arousal-related characteristics, including physiological changes, sleep-wake cycles, exercise effects, cognitive and information processing functions, various mood states, and a number of concomitants of stress. Finally, in a reported empirical study designed to investigate the somewhat uncommon self-rating format of this check list, alternative rating scales were employed with the same adjective descriptors. Factor analyses of respondents' ratings indicated that the factor structure of activation descriptors remained essentially the same with each scale. In this context, the importance of the underlying arousal model in relation to the activation descriptors is discussed.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HSPMQSAM\\Thayer - 1986 - Activation-Deactivation Adjective Check List Curr.pdf}
}

@article{thayer_1986a,
  title = {Activation-{{Deactivation Adjective Check List}}: {{Current Overview}} and {{Structural Analysis}}},
  shorttitle = {Activation-{{Deactivation Adjective Check List}}},
  author = {Thayer, Robert E.},
  year = {1986},
  month = apr,
  journal = {Psychological Reports},
  volume = {58},
  number = {2},
  pages = {607--614},
  issn = {0033-2941, 1558-691X},
  doi = {10.2466/pr0.1986.58.2.607},
  abstract = {The Activation-Deactivation Adjective Check List (AD ACL) is a multidimensional self-rating test constructed and extensively validated for rapid assessments of momentary activation or arousal states. The two core dimensions, energetic arousal (including tiredness) and tense arousal (including calmness) have been replicated repeatedly. They bear a complex relationship to each other involving positive and negative correlations at different levels of intensity. The dimensions are associated with a variety of arousal-related characteristics, including physiological changes, sleep-wake cycles, exercise effects, cognitive and information processing functions, various mood states, and a number of concomitants of stress. Finally, in a reported empirical study designed to investigate the somewhat uncommon self-rating format of this check list, alternative rating scales were employed with the same adjective descriptors. Factor analyses of respondents' ratings indicated that the factor structure of activation descriptors remained essentially the same with each scale. In this context, the importance of the underlying arousal model in relation to the activation descriptors is discussed.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\WD9DVLGA\\Thayer - 1986 - Activation-Deactivation Adjective Check List Curr.pdf}
}

@article{themqdatasciencegroup_2019,
  title = {How Data Science Can Advance Mental Health Research},
  author = {{the MQ Data Science group} and Russ, Tom C. and Woelbert, Eva and Davis, Katrina A. S. and Hafferty, Jonathan D. and Ibrahim, Zina and Inkster, Becky and John, Ann and Lee, William and Maxwell, Margaret and McIntosh, Andrew M. and Stewart, Rob},
  year = {2019},
  month = jan,
  journal = {Nature Human Behaviour},
  volume = {3},
  number = {1},
  pages = {24--32},
  issn = {2397-3374},
  doi = {10.1038/s41562-018-0470-9},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NHQJXETV\\the MQ Data Science group et al. - 2019 - How data science can advance mental health researc.pdf}
}

@article{thirion_2007,
  title = {Analysis of a Large {{fMRI}} Cohort: {{Statistical}} and Methodological Issues for Group Analyses},
  shorttitle = {Analysis of a Large {{fMRI}} Cohort},
  author = {Thirion, Bertrand and Pinel, Philippe and M{\'e}riaux, S{\'e}bastien and Roche, Alexis and Dehaene, Stanislas and Poline, Jean-Baptiste},
  year = {2007},
  month = mar,
  journal = {NeuroImage},
  volume = {35},
  number = {1},
  pages = {105--120},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2006.11.054},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\3XC28NXP\\Thirion_NI_07.pdf}
}

@article{thompson_2017,
  title = {Consent, Ethics and Genetic Biobanks: The Case of the {{Athlome}} Project},
  shorttitle = {Consent, Ethics and Genetic Biobanks},
  author = {Thompson, Rachel and McNamee, Michael J.},
  year = {2017},
  month = nov,
  journal = {BMC Genomics},
  volume = {18},
  number = {S8},
  issn = {1471-2164},
  doi = {10.1186/s12864-017-4189-1},
  abstract = {This article provides a critical overview of the ethics and governance of genetic biobank research, using the Athlome Consortium as a large scale instance of collaborative sports genetic biobanking. We present a traditional model of written informed consent for the acquisition, storage, sharing and analysis of genetic data and articulate the challenges to it from new research practices such as genetic biobanking. We then articulate six possible alternative consent models: verbal consent, blanket consent, broad consent, meta consent, dynamic consent and waived consent. We argue that these models or conceptions of consent must be articulated in the context of the complexities of international legislation and non legislative national and international biobank governance frameworks and policies, those which govern research in the field of sports genetics. We discuss the tensions between individual rights and public benefits of genomic research as a critical ethical issue, particularly where benefits are less obvious, as in sports genomics. The inherent complexities of international regulation and biobanking governance are challenging in a relatively young field. We argue that there is much nuanced ethical work still to be done with regard to governance of sports genetic biobanking and the issues contained therein.},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\SDAPLGPJ\\Thompson and McNamee - 2017 - Consent, ethics and genetic biobanks the case of .pdf}
}

@article{thorogood_2017,
  title = {Substitute Consent to Data Sharing: A Way Forward for International Dementia Research?},
  shorttitle = {Substitute Consent to Data Sharing},
  author = {Thorogood, Adrian and {Desch{\^e}nes~St-Pierre}, Constance and Knoppers, Bartha Maria},
  year = {2017},
  month = jan,
  journal = {Journal of Law and the Biosciences},
  pages = {lsw063},
  issn = {2053-9711},
  doi = {10.1093/jlb/lsw063},
  abstract = {A deluge of genetic and health-related data is being generated about patients with dementia. International sharing of these data accelerates dementia research. Seeking consent to data sharing is a challenge for dementia research where patients have lost or risk losing legal capacity. The laws of most countries enable substitute decision makers (SDMs) to consent on behalf of incapable adults to research participation. We compare regulatory frameworks governing capacity, research, and personal data protection across eight countries to determine when SDMs can consent to data sharing. In most countries, an SDM can consent to data sharing in the incapable adult's best interests. Best interests typically include consideration of the individual's previously expressed wishes, values and beliefs; well-being; and inclusion in decision making. Countries differ in how these considerations are balanced. A clear previous consent or refusal to share data typically binds the discretion of an SDM. Though generally permissive, National patchworks of laws and guidelines cause confusion. Clarity on the applicable law and processes to enhance ethical decision making are needed to facilitate substitute consent. Researchers can encourage patients to communicate their research preferences before a loss of capacity, and educate SDMs about their ethical and legal duties. The research community must also continue to promote the importance of data sharing in dementia.},
  langid = {english},
  annotation = {00003},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9VN6QX8N\\Thorogood et al. - 2017 - Substitute consent to data sharing a way forward .pdf;C\:\\Users\\dirko\\Zotero\\storage\\T597U4Z9\\Thorogood et al. - 2017 - Substitute consent to data sharing a way forward .pdf}
}

@article{thyreau_2012,
  title = {Very Large {{fMRI}} Study Using the {{IMAGEN}} Database: {{Sensitivity}}\textendash Specificity and Population Effect Modeling in Relation to the Underlying Anatomy},
  shorttitle = {Very Large {{fMRI}} Study Using the {{IMAGEN}} Database},
  author = {Thyreau, Benjamin and Schwartz, Yannick and Thirion, Bertrand and Frouin, Vincent and Loth, Eva and {Vollst{\"a}dt-Klein}, Sabine and Paus, Tomas and Artiges, Eric and Conrod, Patricia J. and Schumann, Gunter and Whelan, Robert and Poline, Jean-Baptiste},
  year = {2012},
  month = may,
  journal = {NeuroImage},
  volume = {61},
  number = {1},
  pages = {295--303},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2012.02.083},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UGDGFH4J\\Thyreau_NI_12.pdf}
}

@book{timm_2002,
  title = {Applied Multivariate Analysis},
  author = {Timm, Neil H.},
  year = {2002},
  series = {Springer Texts in Statistics},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-95347-2},
  langid = {english},
  lccn = {QA278 .T53 2002},
  keywords = {Multivariate analysis},
  annotation = {OCLC: ocm47667254},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8R6SUIAG\\Timm - 2002 - Applied multivariate analysis.pdf}
}

@article{tipping_1999,
  title = {Probabilistic Principal Component Analysis},
  author = {Tipping, Michael E and Bishop, Christopher M},
  year = {1999},
  pages = {12},
  abstract = {Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach to PCA.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\482TNXAN\\Tipping und Bishop - Probabilistic principal component analysis.pdf}
}

@article{tisdall_2020,
  title = {Brain\textendash{{Behavior Associations}} for {{Risk Taking Depend}} on the {{Measures Used}} to {{Capture Individual Differences}}},
  author = {Tisdall, Loreen},
  year = {2020},
  journal = {Frontiers in Behavioral Neuroscience},
  volume = {14},
  pages = {18},
  abstract = {Maladaptive risk taking can have severe individual and societal consequences; thus, individual differences are prominent targets for intervention and prevention. Although brain activation has been shown to be associated with individual differences in risk taking, the directionality of the reported brain\textendash behavior associations is less clear. Here, we argue that one aspect contributing to the mixed results is the low convergence between risk-taking measures, especially between the behavioral tasks used to elicit neural functional markers. To address this question, we analyzed within-participant neuroimaging data for two widely used risk-taking tasks collected from the imaging subsample of the Basel\textendash Berlin Risk Study (N = 116 young human adults). Focusing on core brain regions implicated in risk taking (nucleus accumbens, anterior insula, and anterior cingulate cortex), for the two tasks, we examined group-level activation for risky versus safe choices, as well as associations between local functional markers and various risk-related outcomes, including psychometrically derived risk preference factors. While we observed common group-level activation in the two tasks (notably increased nucleus accumbens activation), individual differences analyses support the idea that the presence and directionality of associations between brain activation and risk taking varies as a function of the risk-taking measures used to capture individual differences. Our results have methodological implications for the use of brain markers for intervention or prevention.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FCLPGPZ2\\Tisdall - 2020 - Brain–Behavior Associations for Risk Taking Depend.pdf}
}

@incollection{tishby_2011,
  title = {Information Theory of Decisions and Actions},
  booktitle = {Perception-Action Cycle},
  author = {Tishby, Naftali and Polani, Daniel},
  year = {2011},
  pages = {601--636},
  publisher = {{Springer}},
  annotation = {00148},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RW7WK69Y\\Tishby Polani 2011.pdf}
}

@article{tjur_2009,
  title = {Coefficients of {{Determination}} in {{Logistic Regression Models}}\textemdash{{A New Proposal}}: {{The Coefficient}} of {{Discrimination}}},
  shorttitle = {Coefficients of {{Determination}} in {{Logistic Regression Models}}\textemdash{{A New Proposal}}},
  author = {Tjur, Tue},
  year = {2009},
  month = nov,
  journal = {The American Statistician},
  volume = {63},
  number = {4},
  pages = {366--372},
  issn = {0003-1305, 1537-2731},
  doi = {10.1198/tast.2009.08210},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LX4EVGTQ\\Tjur - 2009 - Coefficients of Determination in Logistic Regressi.pdf}
}

@article{todorov_2009,
  title = {Efficient Computation of Optimal Actions},
  author = {Todorov, Emanuel},
  year = {2009},
  journal = {Proceedings of the national academy of sciences},
  volume = {106},
  number = {28},
  pages = {11478--11483},
  annotation = {00260},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LQRJMUBP\\Todorov 2009.pdf}
}

@article{toelch_2018,
  title = {Digital Open Science\textemdash{{Teaching}} Digital Tools for Reproducible and Transparent Research},
  author = {Toelch, Ulf and Ostwald, Dirk},
  year = {2018},
  month = jul,
  journal = {PLOS Biology},
  volume = {16},
  number = {7},
  pages = {e2006022},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.2006022},
  abstract = {An important hallmark of science is the transparency and reproducibility of scientific results. Over the last few years, internet-based technologies have emerged that allow for a representation of the scientific process that goes far beyond traditional methods and analysis descriptions. Using these often freely available tools requires a suite of skills that is not necessarily part of a curriculum in the life sciences. However, funders, journals, and policy makers increasingly require researchers to ensure complete reproducibility of their methods and analyses. To close this gap, we designed an introductory course that guides students towards a reproducible science workflow. Here, we outline the course content and possible extensions, report encountered challenges, and discuss how to integrate such a course in existing curricula.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JVIQECBQ\\Toelch und Ostwald - 2018 - Digital open science—Teaching digital tools for re.pdf}
}

@article{tomasello_2011,
  title = {Methodological {{Challenges}} in the {{Study}} of {{Primate Cognition}}},
  author = {Tomasello, M. and Call, J.},
  year = {2011},
  month = dec,
  journal = {Science},
  volume = {334},
  number = {6060},
  pages = {1227--1228},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1213443},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\PB2RDL9H\\Tomasello und Call - 2011 - Methodological Challenges in the Study of Primate .pdf}
}

@inproceedings{tomasi_2010,
  title = {Ultrafast Method for Mapping Local Functional Connectivity Hubs in the Human Brain},
  author = {Tomasi, D and Volkow, N D},
  year = {2010},
  month = aug,
  pages = {4274--4277},
  publisher = {{IEEE}},
  doi = {10.1109/IEMBS.2010.5626180},
  abstract = {Brain networks with energy-efficient hubs might support the high cognitive performance of humans and a better understanding of their organization is of relevance not only for studying normal brain development and plasticity but also neuropsychiatric disorders. Here we propose an ultra-fast method to map the distribution of the functional connectivity density (FCD) in the human brain. The method was tested in 972 subjects from a large repository of magnetic resonance imaging (MRI) time series collected in resting conditions. Consistently across research sites, a region located in the ventral precuneus was the area with the highest FCD, which suggest that this is a prominent functional hub in the brain. In addition regions located in the paracentral lobule, cuneus, inferior parietal, and posterior cingulate cortices had localized high FCD, suggesting that these regions also include prominent functional connectivity hubs.},
  isbn = {978-1-4244-4123-5 978-1-4244-4124-2},
  langid = {english},
  annotation = {00009},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\C7WI3CW3\\Tomasi and Volkow - 2010 - Ultrafast method for mapping local functional conn.pdf}
}

@techreport{tomov_2018,
  type = {Preprint},
  title = {Dissociable {{Neural Correlates}} of {{Uncertainty Underlie Different Exploration Strategies}}},
  author = {Tomov, Momchil S. and Truong, Van Q. and Hundia, Rohan A. and Gershman, Samuel J.},
  year = {2018},
  month = nov,
  institution = {{Neuroscience}},
  doi = {10.1101/478131},
  abstract = {Most real-world decisions involve a delicate balance between exploring unfamiliar alternatives and committing to the best known option. Uncertainty lies at the core of this ``explore-exploit'' dilemma, for if all options were perfectly known, there would be no need to explore. Yet despite the prominent role of uncertainty-guided exploration in decision making, evidence for its neural implementation is still sparse. We investigated this question with model-based fMRI (n = 31) using a two-armed bandit task that independently manipulates two forms of uncertainty underlying different exploration strategies. The relative uncertainty between the two options was correlated with BOLD activity in right rostrolateral prefrontal cortex and drove directed exploration, a strategy that adds an uncertainty bonus to each option. The total uncertainty across the two options was correlated with activity in right dorsolateral prefrontal cortex and drove random exploration, a strategy that increases choice stochasticity in proportion to total uncertainty. The subjective estimates of uncertainty from both regions were predictive of subject choices. Finally, the decision value signal combining relative and total uncertainty to compute choice was reflected in motor cortex activity. The variance of this decision value signal scaled with total uncertainty, consistent with a sampling mechanism for random exploration. Overall, these results are consistent with a hybrid computational architecture in which different uncertainty computations are performed separately and then combined by downstream decision circuits to compute choice.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MUHBGTG2\\Tomov et al. - 2018 - Dissociable Neural Correlates of Uncertainty Under.pdf}
}

@article{tovino_2005,
  title = {The {{Confidentiality}} and {{Privacy Implications}} of {{Functional Magnetic Resonance Imaging}}},
  author = {Tovino, Stacey A.},
  year = {2005},
  month = dec,
  journal = {The Journal of Law, Medicine \& Ethics},
  volume = {33},
  number = {4},
  pages = {844--850},
  issn = {1073-1105, 1748-720X},
  doi = {10.1111/j.1748-720X.2005.tb00550.x},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BAM9AB98\\Tovino - 2005 - The Confidentiality and Privacy Implications of Fu.pdf;C\:\\Users\\dirko\\Zotero\\storage\\FFCZJI2G\\Tovino - 2005 - The Confidentiality and Privacy Implications of Fu.pdf}
}

@article{tran_2015,
  title = {Copula Variational Inference},
  author = {Tran, Dustin and Blei, David M. and Airoldi, Edoardo M.},
  year = {2015},
  month = jun,
  journal = {arXiv:1506.03159 [cs, stat]},
  eprint = {1506.03159},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {We develop a general variational inference method that preserves dependency among the latent variables. Our method uses copulas to augment the families of distributions used in mean-field and structured approximations. Copulas model the dependency that is not captured by the original variational distribution, and thus the augmented variational family guarantees better approximations to the posterior. With stochastic optimization, inference on the augmented distribution is scalable. Furthermore, our strategy is generic: it can be applied to any inference procedure that currently uses the mean-field or structured approach. Copula variational inference has many advantages: it reduces bias; it is less sensitive to local optima; it is less sensitive to hyperparameters; and it helps characterize and interpret the dependency among the latent variables.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Learning,Statistics - Computation,Statistics - Machine Learning,Statistics - Methodology},
  annotation = {00023},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\UYJ79VWT\\Tran et al. - 2015 - Copula variational inference.pdf}
}

@article{trinidad_2010,
  title = {Genomic Research and Wide Data Sharing: {{Views}} of Prospective Participants},
  shorttitle = {Genomic Research and Wide Data Sharing},
  author = {Trinidad, Susan Brown and Fullerton, Stephanie M and Bares, Julie M and Jarvik, Gail P and Larson, Eric B and Burke, Wylie},
  year = {2010},
  month = aug,
  journal = {Genetics in Medicine},
  volume = {12},
  number = {8},
  pages = {486--495},
  issn = {1098-3600, 1530-0366},
  doi = {10.1097/GIM.0b013e3181e38f9e},
  abstract = {Purpose\textemdash Sharing study data within the research community generates tension between two important goods: promoting scientific goals and protecting the privacy interests of study participants. The present study was designed to explore the perceptions, beliefs, and attitudes of research participants and possible future participants regarding genome-wide association studies (GWAS) and repository-based research. Methods\textemdash Focus group sessions with (1) current research participants, (2) surrogate decisionmakers, and (3) three age-defined cohorts (18\textendash 34 years, 35\textendash 50, {$>$}50). Results\textemdash Participants expressed a variety of opinions about the acceptability of wide sharing of genetic and phenotypic information for research purposes through large, publicly accessible data repositories. Most believed that making de-identified study data available to the research community is a social good that should be pursued. Privacy and confidentiality concerns were common, though they would not necessarily preclude participation. Many participants voiced reservations about sharing data with for-profit organizations. Conclusions\textemdash Trust is central in participants' views regarding GWAS data sharing. Further research is needed to develop governance models that enact the values of stewardship.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\8JK9RTTD\\Trinidad et al. - 2010 - Genomic research and wide data sharing Views of p.pdf;C\:\\Users\\dirko\\Zotero\\storage\\T5CDDW2I\\Trinidad et al. - 2010 - Genomic research and wide data sharing Views of p.pdf}
}

@article{trujillo-barreto_2001,
  title = {A Symmetrical {{Bayesian}} Model for {{fMRI}} and {{EEG}}/{{MEG}} Neuroimage Fusion},
  author = {{Trujillo-Barreto}, N. J. and {Martinez-Montes}, E. and {Melie-Garcia}, L. and {Valdes-Sosa}, P.},
  year = {2001},
  journal = {Int. J. Bioelectromagn},
  volume = {3},
  number = {1},
  pages = {1998--2000},
  annotation = {00044},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2QWHDEHE\\Trujillo-Bareta_IJOB_01.pdf}
}

@article{tu_,
  title = {The {{Dirichlet-Multinomial}} and {{Dirichlet-Categorical}} Models for {{Bayesian}} Inference},
  author = {Tu, Stephen},
  pages = {6},
  langid = {english},
  annotation = {00021},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\V3XWJ45C\\Tu - The Dirichlet-Multinomial and Dirichlet-Categorica.pdf}
}

@article{tukey_1977,
  title = {Some Thoughts on Clinical Trials, Especially Problems of Multiplicity},
  author = {Tukey, J. W.},
  year = {1977},
  month = nov,
  journal = {Science},
  volume = {198},
  number = {4318},
  pages = {679--684},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.333584},
  abstract = {Problems of statistical and conceptual design of experiments are exacerberated by ethical issues in many, if not most, clinical trials. Statutory requirements of demonstrated effectiveness are far from being clearly resolved--either qualitatively or quantitatively. Ethics, bolstered by informed consent, are likely to keep us from ever learning the answer to many questions. Unbalanced boundaries, focusing-down designs, historical controls, and not-very-sequential designs are among the possible consequences.},
  copyright = {\textcopyright{} 1977},
  langid = {english},
  pmid = {333584},
  annotation = {00446},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FRJSAP3G\\Tukey_1977_Science_Some thoughts on clinical trials, especially problems of multiplicity.pdf;C\:\\Users\\dirko\\Zotero\\storage\\I46ZYLRM\\tab-pdf.html}
}

@article{turner_2016,
  title = {Why More Is Better: {{Simultaneous}} Modeling of {{EEG}}, {{fMRI}}, and Behavioral Data},
  shorttitle = {Why More Is Better},
  author = {Turner, Brandon M. and Rodriguez, Christian A. and Norcia, Tony M. and McClure, Samuel M. and Steyvers, Mark},
  year = {2016},
  month = mar,
  journal = {NeuroImage},
  volume = {128},
  pages = {96--115},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2015.12.030},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\YB5DN5NS\\Turner et al_2016_NeuroImage_Why more is better.pdf}
}

@article{turner_2018,
  title = {Small Sample Sizes Reduce the Replicability of Task-Based {{fMRI}} Studies},
  author = {Turner, Benjamin O. and Paul, Erick J. and Miller, Michael B. and Barbey, Aron K.},
  year = {2018},
  month = jun,
  journal = {Communications Biology},
  volume = {1},
  number = {1},
  pages = {62},
  issn = {2399-3642},
  doi = {10.1038/s42003-018-0073-z},
  abstract = {Benjamin Turner et al. report an assessment of replicability of task-based fMRI studies as a function of sample size. They find that the degree of replicability for typical samples sizes reported in the literature is modest and advocate for standards requiring larger sample sizes, potentially in excess of N\,=\,100.},
  copyright = {2018 The Author(s)},
  langid = {english},
  annotation = {00001},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9YHHXFKU\\Turner et al_2018_Communications Biology_Small sample sizes reduce the replicability of task-based fMRI studies.pdf;C\:\\Users\\dirko\\Zotero\\storage\\3KF7WRFU\\s42003-018-0073-z.html}
}

@article{twieg_1983,
  title = {The K-Trajectory Formulation of the {{NMR}} Imaging Process with Applications in Analysis and Synthesis of Imaging Methods},
  author = {Twieg, Donald B.},
  year = {1983},
  month = sep,
  journal = {Medical Physics},
  volume = {10},
  number = {5},
  pages = {610--621},
  issn = {2473-4209},
  doi = {10.1118/1.595331},
  abstract = {The fundamental operations of nuclear magnetic resonance (NMR) imaging can be formulated, for a large number of methods, as sampling the object distribution in the Fourier spatial-frequency domain, followed by processing the digitized data (often simply by Fourier transformation) to produce a digital image. In these methods, which include reconstruction from projections, Fourier imaging, spin-warp imaging, and echo-planar imaging, controllable gradient fields determine the points in the spatial-frequency domain which are sampled at any given time during the acquisition of data (the free induction decay, or FID). The detailed time dependence of the resulting trajectory of sample points (the k trajectory) determines the relative weight and accuracy with which image information at each spatial frequency is measured, establishing theoretical limitations on image quality achievable with a given imaging method. We demonstrate here that these considerations may be used to compare the theoretical capabilities of NMR imaging methods, and to derive new imaging methods with optimal theoretical imaging properties.},
  copyright = {\textcopyright{} 1983 American Association of Physicists in Medicine},
  langid = {english},
  keywords = {and techniques,auxiliary equipment,Biomedical imaging,Fourier analysis,fourier transformation,Fourier transforms,Free induction decay,Frequency measurement,Image analysis,image forming,Image forming and processing,image processing,Image reconstruction,image scanners,magnetic resonance spectrometers,magnetization,Medical imaging,microwave and radiowave spectrometers,Non-ionizing radiation equipment and techniques,Nuclear data analysis,nuclear magnetic resonance,Nuclear magnetic resonance,Nuclear magnetic resonance imaging,relaxation time,Spatial analysis,Submillimeter wave,uses},
  annotation = {00498},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2JR7ZYJE\\1.html}
}

@book{uludag_2015,
  title = {{{fMRI}}: From Nuclear Spins to Brain Functions},
  shorttitle = {{{fMRI}}},
  editor = {Uludag, Kamil and Ugurbil, Kamil and Berliner, Lawrence},
  year = {2015},
  series = {Biological Magnetic Resonance},
  number = {30},
  publisher = {{Springer}},
  address = {{New York, NY}},
  isbn = {978-1-4899-7591-1 978-1-4899-7590-4},
  langid = {english},
  annotation = {OCLC: 903284669},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5Q2UG7HG\\(Biological Magnetic Resonance 10) Kâmil Uludağ, Kâmil Uğurbil, Lawrence Berliner - fMRI_ From Nuclear Spins to Brain Functions-Springer (2015).pdf}
}

@book{unger_2000,
  title = {Grundkurs {{Mathematik}}},
  author = {Unger, Luise},
  year = {2000},
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@book{unger_2010,
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@article{uurtio_2018a,
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  author = {Uurtio, Viivi and Monteiro, Jo{\~a}o M. and Kandola, Jaz and {Shawe-Taylor}, John and {Fernandez-Reyes}, Delmiro and Rousu, Juho},
  year = {2018},
  month = jan,
  journal = {ACM Computing Surveys},
  volume = {50},
  number = {6},
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  issn = {0360-0300, 1557-7341},
  doi = {10.1145/3136624},
  abstract = {Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has, for instance, been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis, including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.},
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@book{vaart_1998,
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  publisher = {{Cambridge University Press}},
  address = {{Cambridge, UK ; New York, NY, USA}},
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@article{valdes-hernandez_2016,
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@article{valdes-sosa_2009,
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@book{vanbrunt_2004,
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@article{vanderplas_,
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@article{vanessen_2013,
  title = {The {{WU-Minn Human Connectome Project}}: {{An}} Overview},
  shorttitle = {The {{WU-Minn Human Connectome Project}}},
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  abstract = {The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.},
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@inproceedings{vangael_2008,
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@article{vanhorn_2013,
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  volume = {82},
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  issn = {10538119},
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  abstract = {Neuroimaging and the discipline of cognitive neuroscience have grown together in lock-step with each pushing the other toward an improved ability to explore and examine brain function and form. However successful neuroimaging and the examination of cognitive processes may seem today, the culture of data sharing in these fields remains underdeveloped. In this article, we discuss our own experience in the development of the fMRI Data Center (fMRIDC) \textemdash{} a large-scale effort to gather, curate, and openly share the complete data sets from published research articles of brain activation studies using fMRI. We outline the fMRIDC effort's beginnings, how it operated, note some of the sociological reactions we received, and provide several examples of prominent new studies performed using data drawn from the archive. Finally, we provide comment on what considerations are needed for successful neuroimaging databasing and data sharing as existing and emerging efforts take the next steps in archiving and disseminating the field's valuable and irreplaceable data.},
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@article{vanrullen_2011,
  title = {Four {{Common Conceptual Fallacies}} in {{Mapping}} the {{Time Course}} of {{Recognition}}},
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  abstract = {Determining the moment at which a visual recognition process is completed, or the order in which various processes come into play, are fundamental steps in any attempt to understand human recognition abilities, or to replicate the corresponding hierarchy of neuronal mechanisms within artificial systems. Common experimental paradigms for addressing these questions involve the measurement and/or comparison of backward-masking (or rapid serial visual presentation) psychometric functions and of physiological EEG/MEG/LFP signals (peak latencies, differential activities, single-trial decoding techniques). I review and illustrate four common mistakes that scientists tend to make when using these paradigms, and explain the conceptual fallacies that motivate their reasoning. First, contrary to collective intuition, presentation times, or stimulus-onset asynchrony masking thresholds cannot be taken to reflect, directly or indirectly, the timing of relevant brain processes. Second, psychophysical or electrophysiological measurements should not be compared without assessing potential physical differences between experimental stimulus sets. Third, such comparisons should not be performed in any manner contingent on subjective responses, so as to avoid response biases. Last, the filtering of electrophysiological signals alters their temporal structure, and thus precludes their interpretation in terms of time course. Practical solutions are proposed to overcome these common mistakes.},
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@book{vapnik_1998,
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  series = {Adaptive and Learning Systems for Signal Processing, Communications, and Control},
  publisher = {{Wiley}},
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}

@book{vapnik_2010,
  title = {The {{Nature}} of {{Statistical Learning Theory}}},
  author = {Vapnik, Vladimir},
  year = {2010},
  series = {Information {{Science}} and {{Statistics}}},
  edition = {2., nd ed. Softcover version of original hardcover edition 2000},
  publisher = {{Springer New York}},
  address = {{New York, NY}},
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  annotation = {00065  OCLC: 986507767},
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}

@article{vayena_2014,
  title = {Playing a {{Part}} in {{Research}}? {{University Students}}' {{Attitudes}} to {{Direct-To-Consumer Genomics}}},
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  author = {Vayena, Effy and Ineichen, Christian and Stoupka, Elia and Hafen, Ernst},
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@article{veall_1994,
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  author = {Veall, Michael R. and Zimmermann, Klaus F.},
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  month = may,
  journal = {Quality \& Quantity},
  volume = {28},
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  doi = {10.1007/BF01102759},
  abstract = {Many applied researchers of limited dependent variable models found it disadvantageous that a widely accepted Pseudo-R2 does not exist for this type of estimation. The paper provides guidance for researchers in choosing a Pseudo-R 2 in the binary probit case. The starting point is that R 2 is best understood in the ordinary least squares (OLS) case with continuous data, which is chosen as the reference situation. It is considered which Pseudo-R z is best able to mimic the OLS-R 2. The results are surprisingly clear: a measure suggested by McKelveyZavoina performs the best under our criterion. However, in the more likely case of low PseudoRZ's, a normalization of a measure proposed by Aldrich-Nelson which we suggest is almost as good as the McKelvey-Zavoina, and is in general easier to calculate. We also show that if the underlying R 2 is predicted using cubic regressions given the Pseudo-R 2, all measures perform much better.},
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@misc{venables_2021,
  title = {An {{Introduction}} to {{R}}},
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@article{veroniki_2016,
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  year = {2016},
  month = mar,
  journal = {Research Synthesis Methods},
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  number = {1},
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  doi = {10.1002/jrsm.1164},
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@article{viechtbauer_2005,
  title = {Bias and {{Efficiency}} of {{Meta-Analytic Variance Estimators}} in the {{Random-Effects Model}}},
  author = {Viechtbauer, Wolfgang},
  year = {2005},
  month = sep,
  journal = {Journal of Educational and Behavioral Statistics},
  volume = {30},
  number = {3},
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  abstract = {The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population effects and sampling variance. In this context, the usual goal is to estimate the central tendency and the amount of heterogeneity in the population effect sizes. The amount of heterogeneity in a set of effect sizes has implications regarding the interpretation of the meta-analytic findings and often serves as an indicator for the presence of potential moderator variables. Five population heterogeneity estimators were compared in this article analytically and via Monte Carlo simulations with respect to their bias and efficiency.},
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@article{vilares_2011,
  title = {Bayesian Models: The Structure of the World, Uncertainty, Behavior, and the Brain.},
  author = {Vilares, Iris and Kording, Konrad},
  year = {2011},
  month = apr,
  journal = {Annals of the New York Academy of Sciences},
  volume = {1224},
  pages = {22--39},
  issn = {1749-6632},
  doi = {10.1111/j.1749-6632.2011.05965.x},
  abstract = {Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.},
  pmid = {21486294},
  keywords = {Animals,Bayes Theorem,Behavior,Behavior: physiology,Brain,Brain: physiology,Cues,Environment,Humans,Knowledge,Models,Theoretical,Uncertainty},
  annotation = {00184}
}

@article{visscher_2011,
  title = {Would the Field of Cognitive Neuroscience Be Advanced by Sharing Functional {{MRI}} Data?},
  author = {Visscher, Kristina M and Weissman, Daniel H},
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  journal = {BMC Medicine},
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  abstract = {During the past two decades, the advent of functional magnetic resonance imaging (fMRI) has fundamentally changed our understanding of brainbehavior relationships. However, the data from any one study add only incrementally to the big picture. This fact raises important questions about the dominant practice of performing studies in isolation. To what extent are the findings from any single study reproducible? Are researchers who lack the resources to conduct a fMRI study being needlessly excluded? Is pre-existing fMRI data being used effectively to train new students in the field? Here, we will argue that greater sharing and synthesis of raw fMRI data among researchers would make the answers to all of these questions more favorable to scientific discovery than they are today and that such sharing is an important next step for advancing the field of cognitive neuroscience.},
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@misc{vonrossum_2018,
  title = {Python {{Tutorial}} 3.7.0},
  author = {{von Rossum}, Guido},
  year = {2018},
  journal = {Python Tutorial 3.7.0},
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}

@article{wacholder_2004,
  title = {Assessing the {{Probability That}} a {{Positive Report}} Is {{False}}: {{An Approach}} for {{Molecular Epidemiology Studies}}},
  shorttitle = {Assessing the {{Probability That}} a {{Positive Report}} Is {{False}}},
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@incollection{wagenmakers_2017,
  title = {The {{Need}} for {{Bayesian Hypothesis Testing}} in {{Psychological Science}}},
  booktitle = {Psychological {{Science Under Scrutiny}}},
  author = {Wagenmakers, Eric-Jan and Verhagen, Josine and Ly, Alexander and Matzke, Dora and Steingroever, Helen and Rouder, Jeffrey N. and Morey, Richard D.},
  editor = {Lilienfeld, Scott O. and Waldman, Irwin D.},
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  month = feb,
  pages = {123--138},
  publisher = {{John Wiley \& Sons, Inc.}},
  address = {{Hoboken, NJ, USA}},
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@article{wainwright_2007,
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@article{waller_2017,
  title = {Evaluating the Replicability, Specificity, and Generalizability of Connectome Fingerprints},
  author = {Waller, Lea and Walter, Henrik and Kruschwitz, Johann D. and Reuter, Lucia and M{\"u}ller, Sabine and Erk, Susanne and Veer, Ilya M.},
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  abstract = {Establishing reliable, robust, and unique brain signatures from neuroimaging data is a prerequisite for precision psychiatry, and therefore a highly sought-after goal in contemporary neuroscience. Recently, the procedure of connectome fingerprinting, using brain functional connectivity profiles as such signatures, was shown to be able to accurately identify individuals from a group of 126 subjects from the Human Connectome Project (HCP). However, the specificity and generalizability of this procedure were not tested. In this replication study, we show both for the original and an extended HCP data set (n {$\frac{1}{4}$} 900 subjects), as well as for an additional data set of more commonly acquired imaging quality (n {$\frac{1}{4}$} 84) that (i) although the high accuracy can be replicated for the larger HCP 900 data set, accuracy is (ii) lower for standard neuroimaging data, and, that (iii) connectome fingerprinting may not be specific enough to distinguish between individuals. In addition, both accuracy and specificity are projected to drop considerably as the size of a data set increases. Although the moderate-to-high accuracies do suggest there is a portion of unique variance, our results suggest that connectomes may actually be quite similar across individuals. This outcome may be relevant to how precision psychiatry could benefit from inferences based on functional connectomes.},
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@article{walsh_2012,
  title = {Learning from Experience: {{Event-related}} Potential Correlates of Reward Processing, Neural Adaptation, and Behavioral Choice},
  shorttitle = {Learning from Experience},
  author = {Walsh, Matthew M. and Anderson, John R.},
  year = {2012},
  month = sep,
  journal = {Neuroscience \& Biobehavioral Reviews},
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  abstract = {To behave adaptively, we must learn from the consequences of our actions. Studies using event-related potentials (ERPs) have been informative with respect to the question of how such learning occurs. These studies have revealed a frontocentral negativity termed the feedback-related negativity (FRN) that appears after negative feedback. According to one prominent theory, the FRN tracks the difference between the values of actual and expected outcomes, or reward prediction errors. As such, the FRN provides a tool for studying reward valuation and decision making. We begin this review by examining the neural significance of the FRN. We then examine its functional significance. To understand the cognitive processes that occur when the FRN is generated, we explore variables that influence its appearance and amplitude. Specifically, we evaluate four hypotheses: (1) the FRN encodes a quantitative reward prediction error; (2) the FRN is evoked by outcomes and by stimuli that predict outcomes; (3) the FRN and behavior change with experience; and (4) the system that produces the FRN is maximally engaged by volitional actions.},
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@article{walsh_2014,
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}

@article{wang_2018,
  title = {Prefrontal Cortex as a Meta-Reinforcement Learning System},
  author = {Wang, Jane X. and {Kurth-Nelson}, Zeb and Kumaran, Dharshan and Tirumala, Dhruva and Soyer, Hubert and Leibo, Joel Z. and Hassabis, Demis and Botvinick, Matthew},
  year = {2018},
  month = jun,
  journal = {Nature Neuroscience},
  volume = {21},
  number = {6},
  pages = {860--868},
  issn = {1546-1726},
  doi = {10.1038/s41593-018-0147-8},
  abstract = {Humans and other mammals are prodigious learners, partly because they also `learn how to learn'. Wang and colleagues present a new theory showing how learning to learn may arise from interactions between prefrontal cortex and the dopamine system.},
  copyright = {2018 The Author(s)},
  langid = {english},
  annotation = {00000},
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}

@article{wang_2019,
  ids = {wang_frequentist_2019a},
  title = {Frequentist {{Consistency}} of {{Variational Bayes}}},
  author = {Wang, Yixin and Blei, David M.},
  year = {2019},
  month = jul,
  journal = {Journal of the American Statistical Association},
  volume = {114},
  number = {527},
  pages = {1147--1161},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.2018.1473776},
  abstract = {A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) methods. VB methods tend to be faster while achieving comparable predictive performance. However, there are few theoretical results around VB. In this article, we establish frequentist consistency and asymptotic normality of VB methods. Specifically, we connect VB methods to point estimates based on variational approximations, called frequentist variational approximations, and we use the connection to prove a variational Bernstein\textendash von Mises theorem. The theorem leverages the theoretical characterizations of frequentist variational approximations to understand asymptotic properties of VB. In summary, we prove that (1) the VB posterior converges to the Kullback\textendash Leibler (KL) minimizer of a normal distribution, centered at the truth and (2) the corresponding variational expectation of the parameter is consistent and asymptotically normal. As applications of the theorem, we derive asymptotic properties of VB posteriors in Bayesian mixture models, Bayesian generalized linear mixed models, and Bayesian stochastic block models. We conduct a simulation study to illustrate these theoretical results. Supplementary materials for this article are available online.},
  langid = {english},
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}

@article{waskom_2019,
  title = {Designing and {{Interpreting Psychophysical Investigations}} of {{Cognition}}},
  author = {Waskom, Michael L. and Okazawa, Gouki and Kiani, Roozbeh},
  year = {2019},
  month = oct,
  journal = {Neuron},
  volume = {104},
  number = {1},
  pages = {100--112},
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.09.016},
  langid = {english},
  annotation = {00001},
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}

@book{wasserman_2004,
  title = {All of {{Statistics}}},
  author = {Wasserman, Larry},
  year = {2004},
  annotation = {02214},
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}

@book{wasserman_2006,
  title = {All of Nonparametric Statistics},
  author = {Wasserman, Larry},
  year = {2006},
  series = {Springer Texts in Statistics},
  publisher = {{Springer}},
  address = {{New York}},
  isbn = {978-0-387-25145-5},
  langid = {english},
  lccn = {QA278.8 .W37 2006},
  keywords = {Nonparametric statistics},
  annotation = {01537},
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}

@article{wasserstein_2019,
  title = {Moving to a {{World Beyond}} `` {\emph{p}} {$<$} 0.05''},
  author = {Wasserstein, Ronald L. and Schirm, Allen L. and Lazar, Nicole A.},
  year = {2019},
  month = mar,
  journal = {The American Statistician},
  volume = {73},
  number = {sup1},
  pages = {1--19},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2019.1583913},
  langid = {english},
  annotation = {00000},
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}

@article{wayne_2018,
  title = {Unsupervised {{Predictive Memory}} in a {{Goal-Directed Agent}}},
  author = {Wayne, Greg and Hung, Chia-Chun and Amos, David and Mirza, Mehdi and Ahuja, Arun and {Grabska-Barwinska}, Agnieszka and Rae, Jack and Mirowski, Piotr and Leibo, Joel Z. and Santoro, Adam and Gemici, Mevlana and Reynolds, Malcolm and Harley, Tim and Abramson, Josh and Mohamed, Shakir and Rezende, Danilo and Saxton, David and Cain, Adam and Hillier, Chloe and Silver, David and Kavukcuoglu, Koray and Botvinick, Matt and Hassabis, Demis and Lillicrap, Timothy},
  year = {2018},
  month = mar,
  journal = {arXiv:1803.10760 [cs, stat]},
  eprint = {1803.10760},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called "partial observability". An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {00007},
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}

@book{weaver_1983,
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}

@article{welch_1938,
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  year = {1938},
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}

@book{wellek_2010,
  title = {Testing {{Statistical Hypotheses}} of {{Equivalence}} and {{Noninferiority}}},
  author = {Wellek, Stefan},
  year = {2010},
  month = jun,
  edition = {Zeroth},
  publisher = {{Chapman and Hall/CRC}},
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}

@article{welti_1975,
  title = {{{NMR Fourier Zeugmatography}}},
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}

@book{westfall_2011,
  title = {Multiple Comparisons and Multiple Tests Using {{SAS}}},
  author = {Westfall, Peter H. and Tobias, Randall D. and Wolfinger, Russell D.},
  year = {2011},
  edition = {2. ed., 1. print},
  publisher = {{SAS Publ}},
  address = {{Cary, NC}},
  isbn = {978-1-60764-783-6},
  langid = {english},
  annotation = {00827  OCLC: 838511163},
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}

@article{westling_2019,
  title = {Beyond {{Prediction}}: {{A Framework}} for {{Inference With Variational Approximations}} in {{Mixture Models}}},
  shorttitle = {Beyond {{Prediction}}},
  author = {Westling, T. and McCormick, T. H.},
  year = {2019},
  month = oct,
  journal = {Journal of Computational and Graphical Statistics},
  volume = {28},
  number = {4},
  pages = {778--789},
  issn = {1061-8600, 1537-2715},
  doi = {10.1080/10618600.2019.1609977},
  abstract = {Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise frequently in many settings in the health, social, and biological sciences. Variational inference in a frequentist context works by approximating intractable conditional distributions with a tractable family and optimizing the resulting lower bound on the log-likelihood. The variational objective function is typically less computationally intensive to optimize than the true likelihood, enabling scientists to fit rich models even with extremely large datasets. Despite widespread use, little is known about the general theoretical properties of estimators arising from variational approximations to the log-likelihood, which hinders their use in inferential statistics. In this article, we connect such estimators to profile M-estimation, which enables us to provide regularity conditions for consistency and asymptotic normality of variational estimators. Our theory also motivates three methodological improvements to variational inference: estimation of the asymptotic model-robust covariance matrix, a one-step correction that improves estimator efficiency, and an empirical assessment of consistency. We evaluate the proposed results using simulation studies and data on marijuana use from the National Longitudinal Study of Youth. Supplementary materials for this article are available online.},
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}

@article{white_2020,
  title = {Data Sharing and Privacy Issues in Neuroimaging Research: {{Opportunities}}, Obstacles, Challenges, and Monsters under the Bed},
  shorttitle = {Data Sharing and Privacy Issues in Neuroimaging Research},
  author = {White, Tonya and Blok, Elisabet and Calhoun, Vince D.},
  year = {2020},
  month = jul,
  journal = {Human Brain Mapping},
  pages = {hbm.25120},
  issn = {1065-9471, 1097-0193},
  doi = {10.1002/hbm.25120},
  abstract = {Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who ``give'' their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.},
  langid = {english},
  annotation = {00000},
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}

@article{whiteley_2008,
  title = {Implicit Knowledge of Visual Uncertainty Guides Decisions with Asymmetric Outcomes},
  author = {Whiteley, Louise and Sahani, Maneesh},
  year = {2008},
  month = mar,
  journal = {Journal of Vision},
  volume = {8},
  number = {3},
  pages = {2},
  issn = {1534-7362},
  doi = {10.1167/8.3.2},
  abstract = {Perception is an ``inverse problem,'' in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (Helmholtz, 1856; Marr, 1982) and corrupted by noise (Green \& Swets, 1989), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (Berger, 1985; Cox, 1961). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (Knill \& Pouget, 2004). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain.},
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  annotation = {00104},
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}

@book{wickens_1995,
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  title = {The Geometry of Multivariate Statistics},
  author = {Wickens, Thomas D.},
  year = {1995},
  publisher = {{L. Erlbaum Associates}},
  address = {{Hillsdale, N.J}},
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}

@article{wickham_,
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}

@article{wickham_2014,
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}

@book{wickham_2015,
  title = {Advanced {{R}}},
  author = {Wickham, Hadley},
  year = {2015},
  series = {The {{R}} Series},
  publisher = {{CRC Press}},
  address = {{Boca Raton, FL}},
  isbn = {978-1-4665-8696-3},
  lccn = {QA276.45.R3 W5 2015},
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  annotation = {OCLC: ocn881664644},
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}

@book{wickham_2017,
  title = {R for {{Data Science}}},
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}

@book{wickham_2019,
  title = {Advanced {{R}}, {{Second Edition}}},
  author = {Wickham, Hadley},
  year = {2019},
  publisher = {{CRC Press}},
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}

@article{widmann_2012,
  title = {Filter {{Effects}} and {{Filter Artifacts}} in the {{Analysis}} of {{Electrophysiological Data}}},
  author = {Widmann, Andreas and Schr{\"o}ger, Erich},
  year = {2012},
  journal = {Frontiers in Psychology},
  volume = {3},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2012.00233},
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  annotation = {00109},
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}

@article{widmann_2015,
  title = {Digital Filter Design for Electrophysiological Data \textendash{} a Practical Approach},
  author = {Widmann, Andreas and Schr{\"o}ger, Erich and Maess, Burkhard},
  year = {2015},
  month = jul,
  journal = {Journal of Neuroscience Methods},
  volume = {250},
  pages = {34--46},
  issn = {01650270},
  doi = {10.1016/j.jneumeth.2014.08.002},
  abstract = {Background: Filtering is a ubiquitous step in the preprocessing of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Besides the intended effect of the attenuation of signal components considered as noise, filtering can also result in various unintended adverse filter effects (distortions such as smoothing) and filter artifacts. Method: We give some practical guidelines for the evaluation of filter responses (impulse and frequency response) and the selection of filter types (high-pass/low-pass/band-pass/band-stop; finite/infinite impulse response, FIR/IIR) and filter parameters (cutoff frequencies, filter order and roll-off, ripple, delay and causality) to optimize signal-to-noise ratio and avoid or reduce signal distortions for selected electrophysiological applications. Results: Various filter implementations in common electrophysiology software packages are introduced and discussed. Resulting filter responses are compared and evaluated. Conclusion: We present strategies for recognizing common adverse filter effects and filter artifacts and demonstrate them in practical examples. Best practices and recommendations for the selection and reporting of filter parameters, limitations, and alternatives to filtering are discussed.},
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@article{widrow_1975,
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}

@article{wiener_2016,
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  author = {Wiener, Martin and Sommer, Friedrich~T. and Ives, Zachary~G. and Poldrack, Russell~A. and Litt, Brian},
  year = {2016},
  month = nov,
  journal = {Neuron},
  volume = {92},
  number = {3},
  pages = {617--621},
  issn = {08966273},
  doi = {10.1016/j.neuron.2016.10.037},
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}

@book{wiering_2012,
  title = {Reinforcement Learning},
  author = {Wiering, Meng-Hiot and Otterlo, Yew-Soon},
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@article{wilkinson_1973,
  title = {Symbolic {{Description}} of {{Factorial Models}} for {{Analysis}} of {{Variance}}},
  author = {Wilkinson, G. N. and Rogers, C. E.},
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  abstract = {The paper describes the symbolic notation and syntax for specifying factorial models for analysis of variance in the control language of the GENSTAT statistical program system at Rothamsted. The notation generalizes that of Nelder (1965). Algorithm AS 65 (Rogers, 1973) converts factorial model formulae in this notation to a list of model terms represented as binary integers.},
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}

@article{wilks_1932,
  title = {Certain {{Generalizations}} in the {{Analysis}} of {{Variance}}},
  author = {Wilks, S. S.},
  year = {1932},
  journal = {Biometrika},
  volume = {24},
  number = {3-4},
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@misc{wilson_2010,
  title = {F Distributions},
  author = {Wilson, Steven},
  year = {2010},
  journal = {Milefoot}
}

@article{wilson_2014,
  ids = {wilson2014,wilson_humans_2014-1},
  title = {Humans Use Directed and Random Exploration to Solve the Explore\textendash Exploit Dilemma.},
  author = {Wilson, Robert C and Geana, Andra and White, John M and Ludvig, Elliot A and Cohen, Jonathan D},
  year = {2014},
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  number = {6},
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}

@article{wilson_2014a,
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  author = {Wilson, Robert C. and Takahashi, Yuji K. and Schoenbaum, Geoffrey and Niv, Yael},
  year = {2014},
  journal = {Neuron},
  volume = {81},
  number = {2},
  pages = {267--278},
  issn = {08966273},
  doi = {10.1016/j.neuron.2013.11.005},
  abstract = {Orbitofrontal cortex (OFC) has long been known to play an important role in decision making. However, the exact nature of that role has remained elusive. Here, we propose a unifying theory of OFC function. We hypothesize that OFC provides an abstraction of currently available information in the form of a labeling of the current task state, which is used for reinforcement learning (RL) elsewhere in the brain. This function is especially critical when task states include unobservable information, for instance, from working memory. We use this framework to explain classic findings in reversal learning, delayed alternation, extinction, and devaluation as well as more recent findings showing the effect of OFC lesions on the firing of dopaminergic neurons in ventral tegmental area (VTA) in rodents performing an RL task. In addition, we generate a number of testable experimental predictions that can distinguish our theory from other accounts of OFC function. \textcopyright{} 2014 Elsevier Inc.},
  pmid = {24462094},
  annotation = {00319}
}

@article{wilson_2019,
  ids = {wilson2019},
  title = {Ten Simple Rules for the Computational Modeling of Behavioral Data},
  author = {Wilson, Robert C and Collins, Anne GE},
  year = {2019},
  month = nov,
  journal = {eLife},
  volume = {8},
  pages = {e49547},
  publisher = {{eLife Sciences Publications Limited}},
  issn = {2050-084X},
  doi = {10.7554/eLife.49547},
  abstract = {Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.},
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}

@article{wimmer_2020,
  title = {Episodic Memory Retrieval Success Is Associated with Rapid Replay of Episode Content},
  author = {Wimmer, G. Elliott and Liu, Yunzhe and Vehar, Ne{\v z}a and Behrens, Timothy E. J. and Dolan, Raymond J.},
  year = {2020},
  month = jun,
  journal = {Nature Neuroscience},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-020-0649-z},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HKS38D7N\\Wimmer et al. - 2020 - Episodic memory retrieval success is associated wi.pdf}
}

@article{woo_2014,
  title = {Cluster-Extent Based Thresholding in {{fMRI}} Analyses: {{Pitfalls}} and Recommendations},
  shorttitle = {Cluster-Extent Based Thresholding in {{fMRI}} Analyses},
  author = {Woo, Choong-Wan and Krishnan, Anjali and Wager, Tor D.},
  year = {2014},
  month = may,
  journal = {NeuroImage},
  volume = {91},
  pages = {412--419},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2013.12.058},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VZVS98K9\\Woo et al. - 2014 - Cluster-extent based thresholding in fMRI analyses.pdf}
}

@article{woolrich_2004,
  title = {Constrained Linear Basis Sets for {{HRF}} Modelling Using {{Variational Bayes}}},
  author = {Woolrich, Mark W. and Behrens, Timothy E.J. and Smith, Stephen M.},
  year = {2004},
  month = apr,
  journal = {NeuroImage},
  volume = {21},
  number = {4},
  pages = {1748--1761},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2003.12.024},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\5U46MGX6\\Woolrich_Behrens_Smith_NI_04.pdf}
}

@article{works:_1926,
  title = {The {{Choice}} of a {{Class Interval}}},
  author = {{work(s):}, Herbert A. Sturges Reviewed},
  year = {1926},
  journal = {Journal of the American Statistical Association},
  volume = {21},
  number = {153},
  pages = {65--66},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\EGXZGZXD\\work(s) - 1926 - The Choice of a Class Interval.pdf}
}

@article{worldmedicalassociation_2013,
  title = {Declaration of {{Helsinki}}: {{Ethical Principles}} for {{Medical Research Involving Human Subjects}}},
  shorttitle = {World {{Medical Association Declaration}} of {{Helsinki}}},
  author = {World Medical Association},
  year = {2013},
  month = nov,
  journal = {JAMA},
  volume = {310},
  number = {20},
  pages = {2191},
  issn = {0098-7484},
  doi = {10.1001/jama.2013.281053},
  langid = {english},
  annotation = {10703},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KRK3JEHL\\2013 - World Medical Association Declaration of Helsinki.pdf}
}

@article{worsley_1992,
  title = {A {{Three-Dimensional Statistical Analysis}} for {{CBF Activation Studies}} in {{Human Brain}}},
  author = {Worsley, K. J. and Evans, A. C. and Marrett, S. and Neelin, P.},
  year = {1992},
  month = nov,
  journal = {Journal of Cerebral Blood Flow \& Metabolism},
  volume = {12},
  number = {6},
  pages = {900--918},
  issn = {0271-678X, 1559-7016},
  doi = {10.1038/jcbfm.1992.127},
  langid = {english},
  annotation = {02000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZSHR5DBX\\Worsley et al_1992_Journal of Cerebral Blood Flow & Metabolism_A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human.pdf}
}

@article{worsley_1994,
  title = {Local {{Maxima}} and the {{Expected Euler Characteristic}} of {{Excursion Sets}} of {$\chi$} 2, {{F}} and t {{Fields}}},
  author = {Worsley, K. J.},
  year = {1994},
  month = mar,
  journal = {Advances in Applied Probability},
  volume = {26},
  number = {01},
  pages = {13--42},
  issn = {0001-8678, 1475-6064},
  doi = {10.2307/1427576},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\HR52UY2H\\Worsley_1994_Advances in Applied Probability_Local Maxima and the Expected Euler Characteristic of Excursion Sets of χ 2, F.pdf}
}

@article{worsley_1995,
  title = {Boundary Corrections for the Expected {{Euler}} Characteristic of Excursion Sets of Random Fields, with an Application to Astrophysics},
  author = {Worsley, K. J.},
  year = {1995},
  month = dec,
  journal = {Advances in Applied Probability},
  volume = {27},
  number = {4},
  pages = {943--959},
  issn = {0001-8678, 1475-6064},
  doi = {10.2307/1427930},
  abstract = {Certain images arising in astrophysics and medicine are modelled as smooth random fields inside a fixed region, and experimenters are interested in the number of `peaks', or more generally, the topological structure of `hot-spots' present in such an image. This paper studies the Euler characteristic of the excursion set of a random field; the excursion set is the set of points where the image exceeds a fixed threshold, and the Euler characteristic counts the number of connected components in the excursion set minus the number of `holes'. For high thresholds the Euler characteristic is a measure of the number of peaks. The geometry of excursion sets has been studied by Adler (1981) who gives the expectation of two excursion set characteristics, called the DT (differential topology) and IG (integral geometry) characteristics, which equal the Euler characteristic provided the excursion set does not touch the boundary of the region. Worsley (1995) finds a boundary correction which gives the expectation of the Euler characteristic itself in two and three dimensions. The proof uses a representation of the Euler characteristic given by Hadwiger (1959). The purpose of this paper is to give a general result for any number of dimensions. The proof takes a different approach and uses a representation from Morse theory. Results are applied to the recently discovered anomalies in the cosmic microwave background radiation, thought to be the remnants of the creation of the universe.},
  langid = {english},
  keywords = {60G15,DIFFERENTIAL TOPOLOGY,IMAGE ANALYSIS,INTEGRAL GEOMETRY,MORSE THEORY},
  annotation = {00122},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7LDSBLBU\\WORSLEY and University - BOUNDARY CORRECTIONS FOR THE EXPECTED EULER CHARAC.pdf;C\:\\Users\\dirko\\Zotero\\storage\\PMN6K9S3\\436FC506B9B03D0C2D473AC91D86321A.html}
}

@article{worsley_1995a,
  title = {Estimating the {{Number}} of {{Peaks}} in a {{Random Field Using}} the {{Hadwiger Characteristic}} of {{Excursion Sets}}, with {{Applications}} to {{Medical Images}}},
  author = {Worsley, K. J.},
  year = {1995},
  month = apr,
  journal = {The Annals of Statistics},
  volume = {23},
  number = {2},
  pages = {640--669},
  issn = {0090-5364, 2168-8966},
  doi = {10.1214/aos/1176324540},
  abstract = {Certain three-dimensional images arising in medicine and astrophysics are modelled as a smooth random field, and experimenters are interested in the number of peaks or "hot-spots" present in such an image. This paper studies the Hadwiger characteristic of the excursion set of a random field. The excursion set is the set of points where the image exceeds a fixed threshold, and the Hadwiger characteristic, like the Euler characteristic, counts the number of connected components in the excursion set minus the number of "holes." For high thresholds the Hadwiger characteristic is a measure of the number of peaks. The geometry of excursion sets has been studied by Adler, who defined the IG (integral geometry) characteristic of excursion sets as a multidimensional analogue of the number of "upcrossings" of the threshold by a unidimensional process. The IG characteristic equals the Euler characteristic of an excursion set provided that the set does not touch the boundary of the volume, and Adler found the expected IG characteristic for a stationary random field inside a fixed volume. Worsley, Evans, Marrett and Neelin used the IG characteristic as an estimator of the number of regions of activation of positron emission tomography (PET) images of blood flow in the brain. Unfortunately the IG characteristic is only defined on intervals: it is not invariant under rotations and it can fail to count connected regions that touch the boundary. This is important since activation often occurs in the cortical regions near the boundary of the brain. In this paper we study the Hadwiger characteristic, which is defined on arbitrary sets, is invariant under rotations and does count connected regions whether they touch the boundary or not. Our main result is a simple expression for the expected Hadwiger characteristic for an isotropic stationary random field in two and three dimensions and on a smooth surface embedded in three dimensions. Results are applied to PET studies of pain perception and word recognition.},
  langid = {english},
  mrnumber = {MR1332586},
  zmnumber = {0898.62120},
  keywords = {Euler characteristic,geometric probability,image analysis,integral geometry,stereology},
  annotation = {00125},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\KA7ANBAU\\Worsley_1995_The Annals of Statistics_Estimating the Number of Peaks in a Random Field Using the Hadwiger.pdf;C\:\\Users\\dirko\\Zotero\\storage\\HGE392D7\\1176324540.html}
}

@article{worsley_1996,
  title = {A Unified Statistical Approach for Determining Significant Signals in Images of Cerebral Activation},
  author = {Worsley, Keith J. and Marrett, Sean and Neelin, Peter and Vandal, Alain C. and Friston, Karl J. and Evans, Alan C.},
  year = {1996},
  journal = {Human brain mapping},
  volume = {4},
  number = {1},
  pages = {58--73},
  annotation = {02431},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\XVGXNYQ7\\Worsley et al_1996_Human brain mapping_A unified statistical approach for determining significant signals in images of.pdf}
}

@article{worsley_1996a,
  title = {An Unbiased Estimator for the Roughness of a Multivariate {{Gaussian}} Random Field},
  author = {Worsley, K J},
  year = {1996},
  journal = {Technical Report},
  pages = {5},
  abstract = {Images from positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are often modelled as stationary Gaussian random fields, and a general linear model is used to test for the effect of explanatory varaibles on a set of such images (Friston et al., 1994; Worsley and Friston, 1995). Thompson et al. (1996) have modelled displacements of brain surfaces as a multivariate Gaussian random field. In order to test for significant local maxima in such fields using the theory of Adler (1981) and its recent refinements (Worsley, 1994, 1995a, 1995b; Siegmund and Worsley, 1995), we need to estimate the roughness of such fields. This is defined as the variance matrix of the derivative of the random field in each dimension. Some methods have been given by Worlsey et al. (1992) for the special case of stationary variance of the random field, and where the random field is sampled on a uniform lattice. In this note we generalise to the case of multivariate Gaussian data with unknown non-stationary variance matrix, and non-lattice sampling. This latter is particularly important for studying the displacement of brain surfaces, which will be the subject of a future publication.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NL9QAMVU\\Worsley - An unbiased estimator for the roughness of a multi.pdf}
}

@article{worsley_1996b,
  title = {The {{Geometry}} of {{Random Images}}},
  author = {Worsley, Keith J.},
  year = {1996},
  month = jan,
  journal = {CHANCE},
  volume = {9},
  number = {1},
  pages = {27--40},
  issn = {0933-2480, 1867-2280},
  doi = {10.1080/09332480.1996.10542483},
  langid = {english},
  annotation = {00112},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\MSLUN364\\Worsley_1996_CHANCE_The Geometry of Random Images.pdf}
}

@article{worsley_1999,
  title = {Detecting Changes in Nonisotropic Images},
  author = {Worsley, K. J. and Andermann, M. and Koulis, T. and MacDonald, D. and Evans, A. C.},
  year = {1999},
  month = sep,
  journal = {Human Brain Mapping},
  volume = {8},
  number = {2-3},
  pages = {98--101},
  issn = {1097-0193},
  doi = {10.1002/(SICI)1097-0193(1999)8:2/3<98::AID-HBM5>3.0.CO;2-F},
  langid = {english},
  annotation = {00351},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\66VW4TL6\\Worsley et al_1999_Human Brain Mapping_Detecting changes in nonisotropic images.pdf;C\:\\Users\\dirko\\Zotero\\storage\\3QVXRFTP\\398AID-HBM53.0.html}
}

@article{worsley_2002,
  title = {A {{General Statistical Analysis}} for {{fMRI Data}}},
  author = {Worsley, K.J. and Liao, C.H. and Aston, J. and Petre, V. and Duncan, G.H. and Morales, F. and Evans, A.C.},
  year = {2002},
  month = jan,
  journal = {NeuroImage},
  volume = {15},
  number = {1},
  pages = {1--15},
  issn = {10538119},
  doi = {10.1006/nimg.2001.0933},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2DXDS9ZS\\Worsley et al_2002_A General Statistical Analysis for fMRI Data.pdf}
}

@article{worsley_2004,
  title = {Unified Univariate and Multivariate Random Field Theory},
  author = {Worsley, Keith J. and Taylor, Jonathan E. and Tomaiuolo, Francesco and Lerch, Jason},
  year = {2004},
  month = jan,
  journal = {NeuroImage},
  volume = {23},
  pages = {S189-S195},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2004.07.026},
  langid = {english},
  annotation = {00312},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\V4QNI778\\Worsley et al. - 2004 - Unified univariate and multivariate random field t.pdf}
}

@article{worthy_2012,
  title = {Working-Memory Load and Temporal Myopia in Dynamic Decision Making.},
  author = {Worthy, Darrell A. and Otto, A. Ross and Maddox, W. Todd},
  year = {2012},
  journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},
  volume = {38},
  number = {6},
  pages = {1640--1658},
  issn = {1939-1285, 0278-7393},
  doi = {10.1037/a0028146},
  abstract = {We examined the role of working-memory (WM) in dynamic decision-making by having participants perform decision-making tasks under single-task or dual-task conditions. In two experiments participants performed dynamic decision-making tasks where they chose one of two options on each trial. The Decreasing option always gave a larger immediate reward, but caused future rewards for both options to decrease. The Increasing option always gave a smaller immediate reward, but caused future rewards for both options to increase. In each experiment we manipulated the reward structure such that the Decreasing option was the optimal choice in one condition and the Increasing option was the optimal choice in the other condition. Behavioral results indicated that Dual-task participants selected the immediately rewarding Decreasing option more often, while Single-task participants selected the Increasing option more often, regardless of which option was optimal. Thus, Dual-task participants performed worse on one type of task but better on the other type. Modeling results showed that Single-task participants' data was most often best fit by a Win-Stay-Lose-Shift (WSLS) rule-based model that tracked differences across trials, while Dual-task participants' data was most often best fit by a Softmax reinforcement learning model that tracked recency-weighted average rewards for each option. This suggests that manipulating WM load affects the degree to which participants focus on the immediate versus delayed consequences of their actions and whether they employ a rule-based WSLS strategy, but it does not necessarily affect how well people weigh the immediate versus delayed benefits when determining the long-term utility of each option.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\RJKSY725\\Worthy et al. - 2012 - Working-memory load and temporal myopia in dynamic.pdf}
}

@article{wu_2018,
  ids = {wu2018},
  title = {Generalization Guides Human Exploration in Vast Decision Spaces},
  author = {Wu, Charley M. and Schulz, Eric and Speekenbrink, Maarten and Nelson, Jonathan D. and Meder, Bj{\"o}rn},
  year = {2018},
  month = dec,
  journal = {Nature Human Behaviour},
  volume = {2},
  number = {12},
  pages = {915--924},
  publisher = {{Nature Publishing Group}},
  issn = {2397-3374},
  doi = {10.1038/s41562-018-0467-4},
  langid = {english},
  annotation = {00027},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9VDP2EUG\\Wu et al. - 2018 - Generalization guides human exploration in vast de.pdf}
}

@article{wuhle_2010,
  title = {Cortical Processing of Near-Threshold Tactile Stimuli: {{An MEG}} Study},
  shorttitle = {Cortical Processing of Near-Threshold Tactile Stimuli},
  author = {W{\"u}hle, Anja and Mertiens, Lena and R{\"u}ter, Johannes and Ostwald, Dirk and Braun, Christoph},
  year = {2010},
  month = may,
  journal = {Psychophysiology},
  volume = {47},
  number = {3},
  pages = {523--534},
  issn = {00485772, 14698986},
  doi = {10.1111/j.1469-8986.2010.00964.x},
  abstract = {In the present study we tested the applicability of a paired-stimulus paradigm for the investigation of near-threshold (NT) stimulus processing in the somatosensory system using magnetoencephalography. Cortical processing of the NT stimuli was studied indirectly by investigating the impact of NT stimuli on the source activity of succeeding suprathreshold test stimuli. We hypothesized that cortical responses evoked by test stimuli are reduced due to the preactivation of the same finger representation by the preceding NT stimulus. We observed attenuation of the magnetic responses in the secondary somatosensory (SII) cortex, with stronger decreases for perceived than for missed NT stimuli. Our data suggest that processing in the primary somatosensory cortex including recovery lasts for o200 ms. Conversely, the occupancy of SII lasts !500 ms, which points to its role in temporal integration and conscious perception of sensory input.},
  langid = {english},
  annotation = {00016},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GGAJZF47\\Wühle et al. - 2010 - Cortical processing of near-threshold tactile stim.pdf}
}

@article{wulff_2018,
  ids = {wulff2018,wulffMetaanalyticReviewTwo2018a},
  title = {A Meta-Analytic Review of Two Modes of Learning and the Description-Experience Gap.},
  author = {Wulff, Dirk U. and {Mergenthaler-Canseco}, Max and Hertwig, Ralph},
  year = {2018},
  month = feb,
  journal = {Psychological Bulletin},
  volume = {144},
  number = {2},
  pages = {140--176},
  publisher = {{American Psychological Association}},
  issn = {1939-1455, 0033-2909},
  doi = {10.1037/bul0000115},
  abstract = {People can learn about the probabilistic consequences of their actions in two ways: One is by consulting descriptions of an action's consequences and probabilities (e.g., reading up on a medication's side effects). The other is by personally experiencing the probabilistic consequences of an action (e.g., beta testing software). In principle, people taking each route can reach analogous states of knowledge and consequently make analogous decisions. In the last dozen years, however, research has demonstrated systematic discrepancies between description- and experienced-based choices. This descriptionexperience gap has been attributed to factors including reliance on a small set of experience, the impact of recency, and different weighting of probability information in the two decision types. In this meta-analysis focusing on studies using the sampling paradigm of decisions from experience, we evaluated these and other determinants of the decision\textendash{} experience gap by reference to more than 70,000 choices made by more than 6,000 participants. We found, first, a robust description-experience gap but also a key moderator, namely, problem structure. Second, the largest determinant of the gap was reliance on small samples and the associated sampling error: free to terminate search, individuals explored too little to experience all possible outcomes. Third, the gap persisted when sampling error was basically eliminated, suggesting other determinants. Fourth, the occurrence of recency was contingent on decision makers' autonomy to terminate search, consistent with the notion of optional stopping. Finally, we found indications of different probability weighting in decisions from experience versus decisions from description when the problem structure involved a risky and a safe option.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\JSTDZAPT\\Wulff et al. - 2018 - A meta-analytic review of two modes of learning an.pdf;C\:\\Users\\dirko\\Zotero\\storage\\XKZNQZN6\\Wulff et al. - 2018 - A meta-analytic review of two modes of learning an.pdf}
}

@article{wunderlich_2012,
  title = {Mapping Value Based Planning and Extensively Trained Choice in the Human Brain},
  author = {Wunderlich, Klaus and Dayan, Peter and Dolan, Raymond J.},
  year = {2012},
  journal = {Nature Neuroscience},
  volume = {15},
  number = {5},
  pages = {786--791},
  issn = {10976256},
  doi = {10.1038/nn.3068},
  abstract = {Investigations of the underlying mechanisms of choice in humans have focused on learning from prediction errors, leaving the computational structure of value based planning comparatively underexplored. Using behavioral and neuroimaging analyses of a minimax decision task, we found that the computational processes underlying forward planning are expressed in the anterior caudate nucleus as values of individual branching steps in a decision tree. In contrast, values represented in the putamen pertain solely to values learned during extensive training. During actual choice, both striatal areas showed a functional coupling to ventromedial prefrontal cortex, consistent with this region acting as a value comparator. Our findings point toward an architecture of choice in which segregated value systems operate in parallel in the striatum for planning and extensively trained choices, with medial prefrontal cortex integrating their outputs.},
  pmid = {22406551},
  annotation = {00204}
}

@article{xie_,
  title = {Bookdown: {{Authoring Books}} and {{Technical Documents}} with {{R Markdown}}},
  author = {Xie, Yihui},
  pages = {133},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\7KB4UZ5T\\Xie - bookdown Authoring Books and Technical Documents .pdf}
}

@book{xie_2018,
  title = {R {{Markdown}}: The Definitive Guide},
  shorttitle = {R {{Markdown}}},
  author = {Xie, Yihui and Allaire, J. J. and Grolemund, Garrett},
  year = {2018},
  publisher = {{Taylor \& Francis, CRC Press}},
  address = {{Boca Raton}},
  isbn = {978-1-138-35933-8},
  lccn = {TK5105.8885.M39 X393 2018},
  keywords = {Computer programs,Markdown (Document markup language),R (Computer program language),Web site development},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\2VAZ8WGN\\(The R Series) Yihui Xie, Joseph J. Allaire, Garrett Grolemund - R Markdown_ The Definitive Guide (conversion)-CRC Press (2019).pdf}
}

@article{xie_a,
  ids = {xie_dynamic_a},
  title = {Dynamic {{Documents}} with {{R}} and Knitr},
  author = {Xie, Yihui},
  pages = {294},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\LA2YCMIR\\Xie - Dynamic Documents with R and knitr.pdf}
}

@article{yakir_2013,
  title = {Extremes in {{Random Fields}}},
  author = {Yakir, Benjamin},
  year = {2013},
  pages = {254},
  langid = {english},
  annotation = {00017},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\K6NMTZD5\\Yakir_2013_Extremes in Random Fields.pdf;C\:\\Users\\dirko\\Zotero\\storage\\V2UQ97XU\\Yakir - Extremes in Random Fields.pdf}
}

@article{yang_1999,
  title = {A Conversation with {{Lucien Le Cam}}},
  author = {Yang, Grace L.},
  year = {1999},
  month = may,
  journal = {Statistical Science},
  volume = {14},
  number = {2},
  pages = {223--241},
  issn = {0883-4237},
  doi = {10.1214/ss/1009212249},
  abstract = {Lucien Le Cam is currently Emeritus Professor of Mathematics and Statistics at the University of California, Berkeley. He was born on November 18, 1924, in Croze, Creuse, France. He received a Licence es Sciences from the University of Paris in 1945, and a Ph.D. in Statistics from the University of California at Berkeley in 1952. He has been on the faculty of the Statistics Department at Berkeley since 1952 except for a year in Montreal, Canada, as the Director of the Centre de Recherches Mathe\textasciiacute matiques (1972\textendash 1973). He served as Chairman of the Department of Statistics at Berkeley (1961\textendash 1965) and was co-editor with J. Neyman of the Berkeley Symposia.},
  langid = {english},
  annotation = {00019},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\FHXWG9E8\\Yang - 1999 - A conversation with Lucien Le Cam.pdf}
}

@article{yao_2001,
  ids = {yao_method_2001a},
  title = {A Method to Standardize a Reference of Scalp {{EEG}} Recordings to a Point at Infinity},
  author = {Yao, Dezhong},
  year = {2001},
  month = nov,
  journal = {Physiological Measurement},
  volume = {22},
  number = {4},
  pages = {693--711},
  issn = {0967-3334, 1361-6579},
  doi = {10.1088/0967-3334/22/4/305},
  abstract = {The effect of an active reference in EEG recording is one of the oldest technical problems in EEG practice. In this paper, a method is proposed to approximately standardize the reference of scalp EEG recordings to a point at infinity. This method is based on the fact that the use of scalp potentials to determine the neural electrical activities or their equivalent sources does not depend on the reference, so we may approximately reconstruct the equivalent sources from scalp EEG recordings with a scalp point or average reference. Then the potentials referenced at infinity are approximately reconstructed from the equivalent sources. As a point at infinity is far from all the possible neural sources, this method may be considered as a reference electrode standardization technique (REST). The simulation studies performed with assumed neural sources included effects of electrode number, volume conductor model and noise on the performance of REST, and the significance of REST in EEG temporal analysis. The results showed that REST is potentially very effective for the most important superficial cortical region and the standardization could be especially important in recovering the temporal information of EEG recordings.},
  langid = {english},
  annotation = {00215},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\GGR8JS78\\Yao - 2001 - A method to standardize a reference of scalp EEG r.pdf;C\:\\Users\\dirko\\Zotero\\storage\\M3FJQ5VL\\Yao - 2001 - A method to standardize a reference of scalp EEG r.pdf}
}

@article{yarkoni_2009,
  title = {Big Correlations in Little Studies: {{Inflated fMRI}} Correlations Reflect Low Statistical Power\textemdash{{Commentary}} on {{Vul}} et al.(2009)},
  shorttitle = {Big Correlations in Little Studies},
  author = {Yarkoni, Tal},
  year = {2009},
  journal = {Perspectives on Psychological Science},
  volume = {4},
  number = {3},
  pages = {294--298},
  annotation = {00240},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\QIEKZUED\\Yarkoni_PPS_09.pdf}
}

@article{yeung_2018,
  title = {An {{Updated Survey}} on {{Statistical Thresholding}} and {{Sample Size}} of {{fMRI Studies}}},
  author = {Yeung, Andy W. K.},
  year = {2018},
  month = jan,
  journal = {Frontiers in Human Neuroscience},
  volume = {12},
  issn = {1662-5161},
  doi = {10.3389/fnhum.2018.00016},
  abstract = {Background: Since the early 2010s, the neuroimaging field has paid more attention to the issue of false positives. Several journals have issued guidelines regarding statistical thresholds. Three papers have reported the statistical analysis of the thresholds used in fMRI literature, but they were published at least 3 years ago and surveyed papers published during 2007\textendash 2012. This study revisited this topic to evaluate the changes in this field. Methods: The PubMed database was searched to identify the task-based (not resting-state) fMRI papers published in 2017 and record their sample sizes, inferential methods (e.g., voxelwise or clusterwise), theoretical methods (e.g., parametric or non-parametric), significance level, cluster-defining primary threshold (CDT), volume of analysis (whole brain or region of interest) and software used. Results: The majority (95.6\%) of the 388 analyzed articles reported statistics corrected for multiple comparisons. A large proportion (69.6\%) of the 388 articles reported main results by clusterwise inference. The analyzed articles mostly used software Statistical Parametric Mapping (SPM), Analysis of Functional NeuroImages (AFNI), or FMRIB Software Library (FSL) to conduct statistical analysis. There were 70.9\%, 37.6\%, and 23.1\% of SPM, AFNI, and FSL studies, respectively, that used a CDT of p {$\leq$} 0.001. The statistical sample size across the articles ranged between 7 and 1,299 with a median of 33. Sample size did not significantly correlate with the level of statistical threshold. Conclusion: There were still around 53\% (142/270) studies using clusterwise inference that chose a more liberal CDT than p = 0.001 (n = 121) or did not report their CDT (n = 21), down from around 61\% reported by Woo et al. (2014). For FSL studies, it seemed that the CDT practice had no improvement since the survey by Woo et al. (2014). A few studies chose unconventional CDT such as p = 0.0125 or 0.004.},
  langid = {english},
  annotation = {00005},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\C55EJAQF\\Yeung - 2018 - An Updated Survey on Statistical Thresholding and .pdf}
}

@article{yoavbenjamini_,
  title = {Controlling the {{False Discovery Rate}}: {{A Practical}} and {{Powerful Approach}} to {{Multiple Testing}}},
  author = {Yoav Benjamini and Yosef Hochberg},
  journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
  volume = {57},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\VD7QM9JS\\Yoav Benjamini and Yosef Hochberg - Controlling the False Discovery Rate A Practical .pdf}
}

@article{yoo_2018,
  title = {Economic {{Choice}} as an {{Untangling}} of {{Options}} into {{Actions}}},
  author = {Yoo, Seng Bum Michael and Hayden, Benjamin Yost},
  year = {2018},
  month = aug,
  journal = {Neuron},
  volume = {99},
  number = {3},
  pages = {434--447},
  issn = {08966273},
  doi = {10.1016/j.neuron.2018.06.038},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\ZGQJUN5U\\Yoo and Hayden - 2018 - Economic Choice as an Untangling of Options into A.pdf}
}

@article{yoshida_2006,
  title = {Resolution of {{Uncertainty}} in {{Prefrontal Cortex}}},
  author = {Yoshida, Wako and Ishii, Shin},
  year = {2006},
  journal = {Neuron},
  volume = {50},
  number = {5},
  pages = {781--789},
  issn = {08966273},
  doi = {10.1016/j.neuron.2006.05.006},
  abstract = {Making optimal decisions in the face of uncertain or incomplete information arises as a common problem in everyday behavior, but the neural processes underlying this ability remain poorly understood. A typical case is navigation, in which a subject has to search for a known goal from an unknown location. Navigating under uncertain conditions requires making decisions on the basis of the current belief about location and updating that belief based on incoming information. Here, we use functional magnetic resonance imaging during a maze navigation task to study neural activity relating to the resolution of uncertainty as subjects make sequential decisions to reach a goal. We show that distinct regions of prefrontal cortex are engaged in specific computational functions that are well described by a Bayesian model of decision making. This permits efficient goal-oriented navigation and provides new insights into decision making by humans. \textcopyright{} 2006 Elsevier Inc. All rights reserved.},
  pmid = {16731515},
  keywords = {SYSNEURO},
  annotation = {00208}
}

@article{youssofzadeh_2015,
  title = {On Self-Feedback Connectivity in Neural Mass Models Applied to Event-Related Potentials},
  author = {Youssofzadeh, Vahab and Prasad, Girijesh and {Wong-Lin}, KongFatt},
  year = {2015},
  month = mar,
  journal = {NeuroImage},
  volume = {108},
  pages = {364--376},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2014.12.067},
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}

@article{yuste_2017,
  title = {Four Ethical Priorities for Neurotechnologies and {{AI}}},
  author = {Yuste, Rafael and Goering, Sara and y Arcas, Blaise Ag{\"u}era and Bi, Guoqiang and Carmena, Jose M. and Carter, Adrian and Fins, Joseph J. and Friesen, Phoebe and Gallant, Jack and Huggins, Jane E. and Illes, Judy and Kellmeyer, Philipp and Klein, Eran and Marblestone, Adam and Mitchell, Christine and Parens, Erik and Pham, Michelle and Rubel, Alan and Sadato, Norihiro and Sullivan, Laura Specker and Teicher, Mina and Wasserman, David and Wexler, Anna and Whittaker, Meredith and Wolpaw, Jonathan},
  year = {2017},
  month = nov,
  journal = {Nature},
  volume = {551},
  number = {7679},
  pages = {159--163},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/551159a},
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  annotation = {00009},
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}

@article{zabell_2008,
  title = {On {{Student}}'s 1908 {{Article}} ``{{The Probable Error}} of a {{Mean}}''},
  author = {Zabell, S. L},
  year = {2008},
  month = mar,
  journal = {Journal of the American Statistical Association},
  volume = {103},
  number = {481},
  pages = {1--7},
  issn = {0162-1459, 1537-274X},
  doi = {10.1198/016214508000000030},
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}

@article{zajkowski_2017,
  title = {A Causal Role for Right Frontopolar Cortex in Directed, but Not Random, Exploration},
  author = {Zajkowski, Wojciech K. and Kossut, Malgorzata and Wilson, Robert C.},
  year = {2017},
  journal = {eLife},
  volume = {6},
  pages = {1--18},
  issn = {2050084X},
  doi = {10.7554/eLife.27430},
  abstract = {The explore-exploit dilemma occurs anytime we must choose between exploring unknown options for information and exploiting known resources for reward. Previous work suggests that people use two different strategies to solve the explore-exploit dilemma: directed exploration driven by information seeking and random exploration driven by decision noise. Here, we show that these two strategies rely on different neural systems. Using transcranial magnetic stimulation to selectively inhibit right frontopolar cortex, we were able to selectively inhibit directed exploration while leaving random exploration intact, suggesting a causal role for right frontopolar cortex in directed, but not random, exploration.},
  pmid = {28914605},
  annotation = {00027}
}

@book{zaki_2014,
  title = {Data {{Mining}} and {{Analysis}}: {{Fundamental Concepts}} and {{Algorithms}}},
  shorttitle = {Data {{Mining}} and {{Analysis}}},
  author = {Zaki, Mohammed J. and Meira, Jr, Wagner},
  year = {2014},
  month = may,
  edition = {First},
  publisher = {{Cambridge University Press}},
  doi = {10.1017/CBO9780511810114},
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}

@article{zarahn_2001,
  title = {A {{Reference Effect Approach}} for {{Power Analysis}} in {{fMRI}}},
  author = {Zarahn, E. and Slifstein, M.},
  year = {2001},
  month = sep,
  journal = {NeuroImage},
  volume = {14},
  number = {3},
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  issn = {10538119},
  doi = {10.1006/nimg.2001.0852},
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}

@article{zeidman_,
  title = {A Tutorial on Group Effective Connectivity Analysis, Part 1: First Level Analysis with {{DCM}} for {{fMRI}}},
  author = {Zeidman, Peter and Jafarian, Amirhossein and Corbin, Nad{\`e}ge and Seghier, Mohamed L and Razi, Adeel and Friston, Karl J},
  pages = {35},
  abstract = {Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this tutorial, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the first part of the tutorial (current paper), we focus on issues specific to DCM for fMRI, unpacking the relevant theory and highlighting practical considerations. In particular, we clarify the assumptions (i.e., priors) used in DCM for fMRI and how to interpret the model parameters. This tutorial is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\C3ERZ24Z\\Zeidman et al. - A tutorial on group effective connectivity analysi.pdf}
}

@article{zeidman_2019,
  title = {A Guide to Group Effective Connectivity Analysis, Part 1: {{First}} Level Analysis with {{DCM}} for {{fMRI}}},
  shorttitle = {A Guide to Group Effective Connectivity Analysis, Part 1},
  author = {Zeidman, Peter and Jafarian, Amirhossein and Corbin, Nad{\`e}ge and Seghier, Mohamed L. and Razi, Adeel and Price, Cathy J. and Friston, Karl J.},
  year = {2019},
  month = jun,
  journal = {NeuroImage},
  pages = {S1053811919305221},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2019.06.031},
  abstract = {Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subjectlevel to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\BQZR26SU\\Zeidman et al. - 2019 - A guide to group effective connectivity analysis, .pdf}
}

@article{zeidman_2019a,
  title = {Optimizing {{Data}} for {{Modeling Neuronal Responses}}},
  author = {Zeidman, Peter and Kazan, Samira M. and Todd, Nick and Weiskopf, Nikolaus and Friston, Karl J. and Callaghan, Martina F.},
  year = {2019},
  month = jan,
  journal = {Frontiers in Neuroscience},
  volume = {12},
  issn = {1662-453X},
  doi = {10.3389/fnins.2018.00986},
  abstract = {In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol\textemdash and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\NHMIZVTK\\Zeidman et al. - 2019 - Optimizing Data for Modeling Neuronal Responses.pdf;C\:\\Users\\dirko\\Zotero\\storage\\TEAGUAHA\\Zeidman et al. - 2019 - Optimizing Data for Modeling Neuronal Responses.pdf}
}

@article{zeidman_a,
  title = {A Tutorial on Group Effective Connectivity Analysis, Part 2: Second Level Analysis with {{PEB}}},
  author = {Zeidman, Peter and Jafarian, Amirhossein and Seghier, Mohamed L and Litvak, Vladimir and Cagnan, Hayriye and Friston, Karl J},
  pages = {28},
  abstract = {This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). This involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. We walk through this approach in detail, using data from a published fMRI experiment that characterised individual differences in hemispheric lateralization in a semantic processing task. The preliminary subject specific DCM analysis is covered in detail in a companion paper. This tutorial is accompanied by the example dataset and step-by-step instructions to reproduce the analyses.},
  langid = {english},
  annotation = {00000},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\CJJDBG99\\Zeidman et al. - A tutorial on group effective connectivity analysi.pdf}
}

@article{zhang_,
  title = {A Simple Approach to {{Bayesian}} Network Computations},
  author = {Zhang, Nevin Lianwen and Poole, David},
  pages = {8},
  abstract = {The general problem of computing posterior probabilities in Bayesian networks is NP-hard (Cooper 1990). However e cient algorithms are often possible for particular applications by exploiting problem structures. It is well understood that the key to the materialization of such a possibility is to make use of conditional independence and work with factorizations of joint probabilities rather than joint probabilities themselves. Di erent exact approaches can be characterized in terms of their choices of factorizations. We propose a new approach which adopts a straightforward way for factorizing joint probabilities. In comparison with the clique tree propagation approach, our approach is very simple. It allows the pruning of irrelevant variables, it accommodates changes to the knowledge base more easily. it is easier to implement. More importantly, it can be adapted to utilize both intercausal independence and conditional independence in one uniform framework. On the other hand, clique tree propagation is better in terms of facilitating precomputations.},
  langid = {english},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\9EC9X3QE\\Zhang und Poole - A simple approach to Bayesian network computations.pdf}
}

@article{zhang_2017,
  title = {Advances in {{Variational Inference}}},
  author = {Zhang, Cheng and Butepage, Judith and Kjellstrom, Hedvig and Mandt, Stephan},
  year = {2017},
  month = nov,
  journal = {arXiv:1711.05597 [cs, stat]},
  eprint = {1711.05597},
  eprinttype = {arxiv},
  primaryclass = {cs, stat},
  abstract = {Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully applied to various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {00048},
  file = {C\:\\Users\\dirko\\Zotero\\storage\\T2BQBQSU\\Zhang et al. - 2017 - Advances in Variational Inference.pdf}
}

@article{zhu_1997,
  title = {L-{{BFGS-B}}: {{Fortran}} Subroutines for Large-Scale Bound-Constrained Optimization},
  shorttitle = {Algorithm 778},
  author = {Zhu, Ciyou and Byrd, Richard H. and Lu, Peihuang and Nocedal, Jorge},
  year = {1997},
  month = dec,
  journal = {ACM Transactions on Mathematical Software},
  volume = {23},
  number = {4},
  pages = {550--560},
  issn = {00983500},
  doi = {10.1145/279232.279236},
  langid = {english},
  annotation = {02278},
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}

@article{zuo_2014,
  title = {An Open Science Resource for Establishing Reliability and Reproducibility in Functional Connectomics},
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  month = dec,
  journal = {Scientific Data},
  volume = {1},
  pages = {140049},
  issn = {2052-4463},
  doi = {10.1038/sdata.2014.49},
  langid = {english},
  annotation = {00141},
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}

@article{zylberberg_2012,
  title = {The Construction of Confidence in a Perceptual Decision},
  author = {Zylberberg, Ariel and Barttfeld, Pablo and Sigman, Mariano},
  year = {2012},
  journal = {Frontiers in Integrative Neuroscience},
  volume = {6},
  issn = {1662-5145},
  doi = {10.3389/fnint.2012.00079},
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}


