Common spatiotemporal processing of visual features shapes object representation
暂无分享,去创建一个
Luca Turella | Emiliano Ricciardi | Pietro Pietrini | Paolo Papale | Andrea Leo | Luca Cecchetti | Giacomo Handjaras | Giulia Malfatti | Monica Betta | Alessandra Cecilia Rampinini
[1] Riitta Salmelin,et al. Tracking neural coding of perceptual and semantic features of concrete nouns , 2012, NeuroImage.
[2] J. W. Johnson. A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression , 2000, Multivariate behavioral research.
[3] R. Passingham,et al. Objects automatically potentiate action: an fMRI study of implicit processing , 2003, The European journal of neuroscience.
[4] Stephen M. Smith,et al. Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.
[5] 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.
[6] Keiji Tanaka,et al. Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.
[7] Antonio Torralba,et al. Statistics of natural image categories , 2003, Network.
[8] I. Biederman. Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.
[9] Charles Kemp,et al. How to Grow a Mind: Statistics, Structure, and Abstraction , 2011, Science.
[10] Bruno A Olshausen,et al. Timecourse of neural signatures of object recognition. , 2003, Journal of vision.
[11] Johan Wagemans,et al. A conceptual framework of computations in mid-level vision , 2014, Front. Comput. Neurosci..
[12] Peter Neri,et al. Global Properties of Natural Scenes Shape Local Properties of Human Edge Detectors , 2011, Front. Psychology.
[13] 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.
[14] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[15] Zijiang J. He,et al. Vertical and horizontal references determined by linear perspective and optic flow information , 2010 .
[16] L. Tyler,et al. Decoding the Cortical Dynamics of Sound-Meaning Mapping , 2017, The Journal of Neuroscience.
[17] Robert Oostenveld,et al. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..
[18] H. Neumann,et al. The Role of Attention in Figure-Ground Segregation in Areas V1 and V4 of the Visual Cortex , 2012, Neuron.
[19] James V. Haxby,et al. CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave , 2016, bioRxiv.
[20] G. Rizzolatti,et al. Evidence for visuomotor priming effect , 1996, Neuroreport.
[21] P. Neri. Semantic Control of Feature Extraction from Natural Scenes , 2014, The Journal of Neuroscience.
[22] John A. Pyles,et al. Comparing visual representations across human fMRI and computational vision. , 2013, Journal of vision.
[23] F. Qiu,et al. Figure-ground mechanisms provide structure for selective attention , 2007, Nature Neuroscience.
[24] L. Tyler,et al. Predicting the Time Course of Individual Objects with MEG , 2014, Cerebral cortex.
[25] V. Lamme,et al. The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.
[26] Thomas E. Nichols,et al. luster-based computational methods for mass univariate analyses f event-related brain potentials / fields : A simulation study , 2022 .
[27] Kim F. Nimon,et al. Understanding the Results of Multiple Linear Regression , 2013 .
[28] 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.
[29] Philip N. Klein,et al. Recognition of shapes by editing their shock graphs , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] 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.
[31] H. Blum. Biological shape and visual science. I. , 1973, Journal of theoretical biology.
[32] 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.
[33] 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.
[34] Radoslaw Martin Cichy,et al. The representational dynamics of task and object processing in humans , 2018, eLife.
[35] Emiliano Ricciardi,et al. Modality-independent encoding of individual concepts in the left parietal cortex , 2017, Neuropsychologia.
[36] D. Field,et al. Natural image statistics and efficient coding. , 1996, Network.
[37] George L. Malcolm,et al. Making Sense of Real-World Scenes , 2016, Trends in Cognitive Sciences.
[38] O. Schwartz,et al. Visual attention and flexible normalization pools. , 2013, Journal of vision.
[39] Antonio Torralba,et al. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.
[40] Peter Neri,et al. Object segmentation controls image reconstruction from natural scenes , 2017, PLoS biology.
[41] Eric T. Carlson,et al. Medial Axis Shape Coding in Macaque Inferotemporal Cortex , 2012, Neuron.
[42] Emiliano Ricciardi,et al. Foreground-Background Segmentation Revealed during Natural Image Viewing , 2017, eNeuro.
[43] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[44] Marius V Peelen,et al. Shape-independent object category responses revealed by MEG and fMRI decoding. , 2016, Journal of neurophysiology.
[45] Irving Biederman,et al. Cortical representation of medial axis structure. , 2013, Cerebral cortex.
[46] M. Bar. A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition , 2003, Journal of Cognitive Neuroscience.
[47] Kechen Zhang,et al. A Sparse Object Coding Scheme in Area V4 , 2011, Current Biology.
[48] H. Blum. Biological shape and visual science (part I) , 1973 .
[49] D H Brainard,et al. The Psychophysics Toolbox. , 1997, Spatial vision.
[50] Kim Nimon,et al. Using commonality analysis in multiple regressions: a tool to decompose regression effects in the face of multicollinearity , 2014 .
[51] 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.
[52] Radoslaw Martin Cichy,et al. Multivariate pattern analysis for MEG: A comparison of dissimilarity measures , 2018, NeuroImage.
[53] 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..
[54] Emiliano Ricciardi,et al. How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge , 2016, NeuroImage.
[55] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[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] J L Gallant,et al. Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.
[58] Jonathan R. Williford,et al. Figure-ground organization in visual cortex for natural scenes , 2016 .
[59] Joachim Gross,et al. Good practice for conducting and reporting MEG research , 2013, NeuroImage.