Neural Responses to Naturalistic Clips of Behaving Animals in Two Different Task Contexts

[1]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[2]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[3]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[4]  J. S. Guntupalli,et al.  The Representation of Biological Classes in the Human Brain , 2012, The Journal of Neuroscience.

[5]  N. Sigala,et al.  Visual categorization shapes feature selectivity in the primate temporal cortex , 2002, Nature.

[6]  James L. McClelland,et al.  The parallel distributed processing approach to semantic cognition , 2003, Nature Reviews Neuroscience.

[7]  Kingson Man,et al.  Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations , 2015, Front. Hum. Neurosci..

[8]  J. Duncan,et al.  Discrimination of Visual Categories Based on Behavioral Relevance in Widespread Regions of Frontoparietal Cortex , 2015, The Journal of Neuroscience.

[9]  D. Heeger,et al.  Reliability of cortical activity during natural stimulation , 2010, Trends in Cognitive Sciences.

[10]  Gidon Felsen,et al.  A natural approach to studying vision , 2005, Nature Neuroscience.

[11]  Stefan Pollmann,et al.  PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data , 2009, Neuroinformatics.

[12]  Christiane Fellbaum,et al.  English Verbs as a Semantic Net , 1990 .

[13]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  D. Heeger,et al.  The Normalization Model of Attention , 2009, Neuron.

[16]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[17]  R. Shepard.  Stimulus and response generalization: tests of a model relating generalization to distance in psychological space. , 1958, Journal of experimental psychology.

[18]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[19]  Bryan R. Conroy,et al.  A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex , 2011, Neuron.

[20]  Janneke F. M. Jehee,et al.  Attention Improves Encoding of Task-Relevant Features in the Human Visual Cortex , 2011, The Journal of Neuroscience.

[21]  Joset A. Etzel.  MVPA significance testing when just above chance, and related properties of permutation tests , 2017, 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

[22]  Brenna Argall,et al.  SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[23]  Satrajit S. Ghosh,et al.  BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods , 2016, bioRxiv.

[24]  Oliver Speck,et al.  A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie , 2014, Scientific Data.

[25]  Krzysztof J. Gorgolewski,et al.  MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites , 2016, bioRxiv.

[26]  Thomas E. Nichols,et al.  Fixing the stimulus-as-fixed-effect fallacy in task fMRI , 2016, bioRxiv.

[27]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[28]  D M Ennis,et al.  Toward a universal law of generalization. , 1988, Science.

[29]  John-Dylan Haynes,et al.  Valid population inference for information-based imaging: From the second-level t-test to prevalence inference , 2015, NeuroImage.

[30]  R. Shepard.  Attention and the metric structure of the stimulus space. , 1964 .

[31]  Alexander G. Huth,et al.  Attention During Natural Vision Warps Semantic Representation Across the Human Brain , 2013, Nature Neuroscience.

[32]  John Duncan,et al.  Evidence for long-range feedback in target detection: Detection of semantic targets modulates activity in early visual areas , 2009, Neuropsychologia.

[33]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[34]  B. Granger.  Ipython: a System for Interactive Scientific Computing Python: an Open and General- Purpose Environment , 2007 .

[35]  Yaroslav O. Halchenko,et al.  The Animacy Continuum in the Human Ventral Vision Pathway , 2015, Journal of Cognitive Neuroscience.

[36]  Peter Gärdenfors,et al.  Using Conceptual Spaces to Model Actions and Events , 2012, J. Semant..

[37]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[38]  J. Gallant,et al.  Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies , 2011, Current Biology.

[39]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[40]  Satrajit S. Ghosh,et al.  Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python , 2011, Front. Neuroinform..

[41]  Jonathan W. Peirce,et al.  PsychoPy—Psychophysics software in Python , 2007, Journal of Neuroscience Methods.

[42]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[43]  J. Maunsell,et al.  Attention improves performance primarily by reducing interneuronal correlations , 2009, Nature Neuroscience.

[44]  Lawrence L. Wald,et al.  Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters , 2005, NeuroImage.

[45]  F ATTNEAVE,et al.  Dimensions of similarity. , 1950, The American journal of psychology.

[46]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[47]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[48]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[49]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[50]  Paul E. Downing,et al.  A comparison of volume-based and surface-based multi-voxel pattern analysis , 2011, NeuroImage.

[51]  Samuel A. Nastase,et al.  Attention Selectively Reshapes the Geometry of Distributed Semantic Representation , 2016, bioRxiv.

[52]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[53]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[55]  Yaroslav O. Halchenko,et al.  Neuroscience Runs on GNU/Linux , 2011, Front. Neuroinform..

[56]  Dwight J. Kravitz,et al.  Task context impacts visual object processing differentially across the cortex , 2014, Proceedings of the National Academy of Sciences.

[57]  D. Heeger,et al.  Categorical Clustering of the Neural Representation of Color , 2013, The Journal of Neuroscience.

[58]  J. I.  The Design of Experiments , 1936, Nature.

[59]  S Edelman,et al.  Representation is representation of similarities , 1996, Behavioral and Brain Sciences.

[60]  Paul E. Downing,et al.  Crossmodal and action-specific: neuroimaging the human mirror neuron system , 2013, Trends in Cognitive Sciences.

[61]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[62]  John T. Serences,et al.  Attention modulates spatial priority maps in the human occipital, parietal and frontal cortices , 2013, Nature Neuroscience.

[63]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[64]  Moritz F. Wurm,et al.  Decoding Actions at Different Levels of Abstraction , 2015, The Journal of Neuroscience.

[65]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[66]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[67]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[68]  Russell A. Poldrack,et al.  OpenfMRI: Open sharing of task fMRI data , 2017, NeuroImage.

[69]  Mark S. Cohen,et al.  Parametric Analysis of fMRI Data Using Linear Systems Methods , 1997, NeuroImage.

[70]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[71]  Michael Hanke,et al.  A studyforrest extension, retinotopic mapping and localization of higher visual areas , 2016, Scientific Data.

[72]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[73]  S. Zeki,et al.  Functional brain mapping during free viewing of natural scenes , 2004, Human brain mapping.

[74]  Marcel A. J. van Gerven,et al.  Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.

[75]  Geoffrey Karl Aguirre,et al.  Continuous carry-over designs for fMRI , 2007, NeuroImage.

[76]  Yaroslav O. Halchenko,et al.  Cross-modal searchlight classification: methodological challenges and recommended solutions , 2016, 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

[77]  N. Sigala,et al.  Visual categorization and the inferior temporal cortex , 2004, Behavioural Brain Research.

[78]  Brian E. Granger,et al.  IPython: A System for Interactive Scientific Computing , 2007, Computing in Science & Engineering.

[79]  T. Poggio,et al.  Cognitive neuroscience: Neural mechanisms for the recognition of biological movements , 2003, Nature Reviews Neuroscience.

[80]  Yi Chen,et al.  Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control , 2011, NeuroImage.

[81]  Jack L. Gallant,et al.  A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain , 2012, Neuron.

[82]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[83]  William W. Graves,et al.  Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. , 2009, Cerebral cortex.

[84]  A. Tversky.  Features of Similarity , 1977 .

[85]  W. R. Garner,et al.  Integrality of stimulus dimensions in various types of information processing , 1970 .

[86]  E. Rosch.  Cognitive Representations of Semantic Categories. , 1975 .

[87]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .