Common spatiotemporal processing of visual features shapes object representation

[1]  Radoslaw Martin Cichy,et al.  Multivariate pattern analysis for MEG: A comparison of dissimilarity measures , 2018, NeuroImage.

[2]  Li Fei-Fei,et al.  Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior , 2018, eLife.

[3]  Radoslaw Martin Cichy,et al.  The representational dynamics of task and object processing in humans , 2018, eLife.

[4]  Peter Neri,et al.  Object segmentation controls image reconstruction from natural scenes , 2017, PLoS biology.

[5]  Emiliano Ricciardi,et al.  Foreground-Background Segmentation Revealed during Natural Image Viewing , 2017, eNeuro.

[6]  Emiliano Ricciardi,et al.  Modality-independent encoding of individual concepts in the left parietal cortex , 2017, Neuropsychologia.

[7]  L. Tyler,et al.  Decoding the Cortical Dynamics of Sound-Meaning Mapping , 2017, The Journal of Neuroscience.

[8]  Doris Y. Tsao,et al.  Consistency of Border-Ownership Cells across Artificial Stimuli, Natural Stimuli, and Stimuli with Ambiguous Contours , 2016, The Journal of Neuroscience.

[9]  George L. Malcolm,et al.  Making Sense of Real-World Scenes , 2016, Trends in Cognitive Sciences.

[10]  Pieter R. Roelfsema,et al.  Texture Segregation Causes Early Figure Enhancement and Later Ground Suppression in Areas V1 and V4 of Visual Cortex , 2016, Cerebral cortex.

[11]  Emiliano Ricciardi,et al.  How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge , 2016, NeuroImage.

[12]  Jonathan R. Williford,et al.  Figure-Ground Organization in Visual Cortex for Natural Scenes , 2016, eNeuro.

[13]  Daria Proklova,et al.  Disentangling Representations of Object Shape and Object Category in Human Visual Cortex: The Animate–Inanimate Distinction , 2016, Journal of Cognitive Neuroscience.

[14]  James V. Haxby,et al.  CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave , 2016, bioRxiv.

[15]  Marius V Peelen,et al.  Shape-independent object category responses revealed by MEG and fMRI decoding. , 2016, Journal of neurophysiology.

[16]  H. P. Op de Beeck,et al.  Dissociations and Associations between Shape and Category Representations in the Two Visual Pathways , 2015, The Journal of Neuroscience.

[17]  Jack L. Gallant,et al.  Fourier power, subjective distance, and object categories all provide plausible models of BOLD responses in scene-selective visual areas , 2015, Front. Comput. Neurosci..

[18]  Thomas E. Nichols,et al.  luster-based computational methods for mass univariate analyses f event-related brain potentials / fields : A simulation study , 2022 .

[19]  Johan Wagemans,et al.  A conceptual framework of computations in mid-level vision , 2014, Front. Comput. Neurosci..

[20]  L. Tyler,et al.  Predicting the Time Course of Individual Objects with MEG , 2014, Cerebral cortex.

[21]  Tom Hartley,et al.  Low-Level Image Properties of Visual Objects Predict Patterns of Neural Response across Category-Selective Regions of the Ventral Visual Pathway , 2014, The Journal of Neuroscience.

[22]  Kim Nimon,et al.  Using commonality analysis in multiple regressions: a tool to decompose regression effects in the face of multicollinearity , 2014 .

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

[24]  P. Neri Semantic Control of Feature Extraction from Natural Scenes , 2014, The Journal of Neuroscience.

[25]  John A. Pyles,et al.  Comparing visual representations across human fMRI and computational vision. , 2013, Journal of vision.

[26]  Kim F. Nimon,et al.  Understanding the Results of Multiple Linear Regression , 2013 .

[27]  Irving Biederman,et al.  Cortical representation of medial axis structure. , 2013, Cerebral cortex.

[28]  O. Schwartz,et al.  Visual attention and flexible normalization pools. , 2013, Journal of vision.

[29]  Joachim Gross,et al.  Good practice for conducting and reporting MEG research , 2013, NeuroImage.

[30]  Riitta Salmelin,et al.  Tracking neural coding of perceptual and semantic features of concrete nouns , 2012, NeuroImage.

[31]  H. Neumann,et al.  The Role of Attention in Figure-Ground Segregation in Areas V1 and V4 of the Visual Cortex , 2012, Neuron.

[32]  Li Su,et al.  Spatiotemporal Searchlight Representational Similarity Analysis in EMEG Source Space , 2012, 2012 Second International Workshop on Pattern Recognition in NeuroImaging.

[33]  Eric T. Carlson,et al.  Medial Axis Shape Coding in Macaque Inferotemporal Cortex , 2012, Neuron.

[34]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[35]  Peter Neri,et al.  Global Properties of Natural Scenes Shape Local Properties of Human Edge Detectors , 2011, Front. Psychology.

[36]  Charles Kemp,et al.  How to Grow a Mind: Statistics, Structure, and Abstraction , 2011, Science.

[37]  Kechen Zhang,et al.  A Sparse Object Coding Scheme in Area V4 , 2011, Current Biology.

[38]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[39]  Zijiang J. He,et al.  Vertical and horizontal references determined by linear perspective and optic flow information , 2010 .

[40]  In-Seuck Jeung,et al.  Investigation of the pseudo-shock wave in a two-dimensional supersonic inlet , 2010, J. Vis..

[41]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

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

[43]  F. Qiu,et al.  Figure-ground mechanisms provide structure for selective attention , 2007, Nature Neuroscience.

[44]  Philip N. Klein,et al.  Recognition of shapes by editing their shock graphs , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Bruno A Olshausen,et al.  Timecourse of neural signatures of object recognition. , 2003, Journal of vision.

[46]  R. Passingham,et al.  Objects automatically potentiate action: an fMRI study of implicit processing , 2003, The European journal of neuroscience.

[47]  M. Bar A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition , 2003, Journal of Cognitive Neuroscience.

[48]  Antonio Torralba,et al.  Statistics of natural image categories , 2003, Network.

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

[50]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[51]  V. Lamme,et al.  The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.

[52]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[53]  J. W. Johnson A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression , 2000, Multivariate behavioral research.

[54]  G. Rizzolatti,et al.  Evidence for visuomotor priming effect , 1996, Neuroreport.

[55]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[56]  D. Field,et al.  Natural image statistics and efficient coding. , 1996, Network.

[57]  Victor A. F. Lamme The neurophysiology of figure-ground segregation in primary visual cortex , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[58]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[59]  H. Blum Biological shape and visual science (part I) , 1973 .

[60]  Kendrick Norris Kay Understanding Visual Representation by Developing Receptive-Field Models , 2011 .

[61]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[62]  H. Blum Biological shape and visual science. I. , 1973, Journal of theoretical biology.