Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices

Invariant object recognition, which refers to the ability of precisely and rapidly recognizing objects in the presence of variations, has been a central question in human vision research. The general consensus is that the ventral and dorsal visual streams are the major processing pathways which undertake category and variation encoding in entangled layers. This overlooks the mounting evidence which support the role of peri-frontal areas in category encoding. These recent studies, however, have left open several aspects of visual processing in peri-frontal areas including whether these areas contributed only in active tasks, whether they interacted with peri-occipital areas or processed information independently and differently. To address these concerns, a passive EEG paradigm was designed in which subjects viewed a set of variation-controlled object images. Using multivariate pattern analysis, noticeable category and variation information were observed in occipital, parietal, temporal and prefrontal areas, supporting their contribution to visual processing. Using task specificity indices, phase and Granger causality analyses, three distinct stages of processing were identified which revealed transfer of information between peri-frontal and peri-occipital areas suggesting their parallel and interactive processing of visual information. A brain-plausible computational model supported the possibility of parallel processing mechanisms in peri-occipital and peri-frontal areas. These findings, while advocating previous results on the role of prefrontal areas in object recognition, extend their contribution from active recognition, in which peri-frontal to peri-occipital feedback mechanisms are activated, to the general case of object and variation processing, which is an integral part of visual processing and play role even during passive viewing.

[1]  Dwight J. Kravitz,et al.  The ventral visual pathway: an expanded neural framework for the processing of object quality , 2013, Trends in Cognitive Sciences.

[2]  Dwight J. Kravitz,et al.  High-level visual object representations are constrained by position. , 2010, Cerebral cortex.

[3]  T. Poggio,et al.  What and where: A Bayesian inference theory of attention , 2010, Vision Research.

[4]  Ha Hong,et al.  Explicit information for category-orthogonal object properties increases along the ventral stream , 2016, Nature Neuroscience.

[5]  Nasour Bagheri,et al.  Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition , 2017, Neuroscience.

[6]  J. Bullier.  Integrated model of visual processing , 2001, Brain Research Reviews.

[7]  Nicole C. Rust,et al.  Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT , 2010, The Journal of Neuroscience.

[8]  S. Thorpe,et al.  The orbitofrontal cortex: Neuronal activity in the behaving monkey , 2004, Experimental Brain Research.

[9]  Daeyeol Lee,et al.  Effects of noise correlations on information encoding and decoding. , 2006, Journal of neurophysiology.

[10]  Doris Y. Tsao,et al.  Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System , 2010, Science.

[11]  Reza Ebrahimpour,et al.  Feedforward object-vision models only tolerate small image variations compared to human , 2014, Front. Comput. Neurosci..

[12]  T. Moore,et al.  CONTROL OF VISUAL CORTICAL SIGNALS BY PREFRONTAL DOPAMINE , 2011, Nature.

[13]  Majid Nili Ahmadabadi,et al.  Temporal dynamics of visual category representation in the macaque inferior temporal cortex. , 2016, Journal of neurophysiology.

[14]  Nicolas Pinto,et al.  Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..

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

[16]  Su Keun Jeong,et al.  Behaviorally Relevant Abstract Object Identity Representation in the Human Parietal Cortex , 2016, The Journal of Neuroscience.

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

[18]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[19]  Sidney R. Lehky,et al.  Frontiers in Computational Neuroscience Computational Neuroscience , 2022 .

[20]  David D. Cox,et al.  Untangling invariant object recognition , 2007, Trends in Cognitive Sciences.

[21]  G. Kreiman,et al.  Timing, Timing, Timing: Fast Decoding of Object Information from Intracranial Field Potentials in Human Visual Cortex , 2009, Neuron.

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  M. Bar,et al.  Magnocellular Projections as the Trigger of Top-Down Facilitation in Recognition , 2007, The Journal of Neuroscience.

[24]  Ankoor S. Shah,et al.  Functional anatomy and interaction of fast and slow visual pathways in macaque monkeys. , 2007, Cerebral cortex.

[25]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[26]  John Duncan,et al.  Dynamic Construction of a Coherent Attentional State in a Prefrontal Cell Population , 2013, Neuron.

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

[28]  Tim Curran,et al.  The Limits of Feedforward Vision: Recurrent Processing Promotes Robust Object Recognition when Objects Are Degraded , 2012, Journal of Cognitive Neuroscience.

[29]  M. Bar,et al.  Cortical Mechanisms Specific to Explicit Visual Object Recognition , 2001, Neuron.

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

[31]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[32]  Marieke Mur,et al.  Extracting Object Identity: Ventral or Dorsal Visual Stream? , 2016, The Journal of Neuroscience.

[33]  Marlene Behrmann,et al.  Retinotopic information interacts with category selectivity in human ventral cortex , 2016, Neuropsychologia.

[34]  A. Norcia,et al.  A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification , 2015, PloS one.

[35]  Martin N Hebart,et al.  What Visual Information Is Processed in the Human Dorsal Stream? , 2012, The Journal of Neuroscience.

[36]  Victor A. F. Lamme,et al.  Feedforward, horizontal, and feedback processing in the visual cortex , 1998, Current Opinion in Neurobiology.

[37]  Christoph Braun,et al.  Feeling before knowing why: The role of the orbitofrontal cortex in intuitive judgments—an MEG study , 2014, Cognitive, affective & behavioral neuroscience.

[38]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[39]  Thomas A. Carlson,et al.  Representational dynamics of object recognition: Feedforward and feedback information flows , 2016, NeuroImage.

[40]  A. Mognon,et al.  ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.

[41]  Margot J. Taylor,et al.  N170 or N1? Spatiotemporal differences between object and face processing using ERPs. , 2004, Cerebral cortex.

[42]  P. Milner.  A model for visual shape recognition. , 1974, Psychological review.

[43]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[44]  Christoph M. Michel,et al.  The Neural Substrates and Timing of Top–Down Processes during Coarse-to-Fine Categorization of Visual Scenes: A Combined fMRI and ERP Study , 2010, Journal of Cognitive Neuroscience.

[45]  Ahmed Abdul Mujeeb Qhusro.  Invariant object recognition , 2004 .

[46]  Nasour Bagheri,et al.  Average activity, but not variability, is the dominant factor in the representation of object categories in the brain , 2017, Neuroscience.

[47]  M. Bozkurt,et al.  Functional anatomy. , 1980, Equine veterinary journal.

[48]  David A. Tovar,et al.  Representational dynamics of object vision: the first 1000 ms. , 2013, Journal of vision.

[49]  Xian Zhang,et al.  Frontal-Occipital Connectivity During Visual Search , 2012, Brain Connect..

[50]  Li Zhaoping,et al.  Understanding Vision: Theory, Models, and Data , 2014 .

[51]  David C. Plaut,et al.  ‘What’ Is Happening in the Dorsal Visual Pathway , 2016, Trends in Cognitive Sciences.

[52]  P. Goldman-Rakic,et al.  Preface: Cerebral Cortex Has Come of Age , 1991 .

[53]  E. Halgren,et al.  Top-down facilitation of visual recognition. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[54]  T. Carlson,et al.  High temporal resolution decoding of object position and category. , 2011, Journal of vision.

[55]  Keiji Tanaka,et al.  Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.

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

[57]  Manfred Fahle,et al.  Ultra Rapid Object Categorization: Effects of Level, Animacy and Context , 2013, PloS one.

[58]  Nancy Kanwisher,et al.  The distribution of category and location information across object-selective regions in human visual cortex , 2008, Proceedings of the National Academy of Sciences.

[59]  Ehud Zohary,et al.  Position and Identity Information Available in fMRI Patterns of Activity in Human Visual Cortex , 2015, The Journal of Neuroscience.

[60]  Blake W. Johnson,et al.  A high density ERP comparison of mental rotation and mental size transformation , 2003, Brain and Cognition.

[61]  Daniel L. K. Yamins,et al.  Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..

[62]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[63]  Nikolaus Kriegeskorte,et al.  Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..

[64]  Nasour Bagheri,et al.  Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models , 2017, Scientific Reports.

[65]  P. Wojtkowski.  Temporal Dynamics , 2017, Encyclopedia of GIS.

[66]  J. M. Hupé,et al.  Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.

[67]  T. Poggio,et al.  Neural mechanisms of object recognition , 2002, Current Opinion in Neurobiology.

[68]  Joel Z. Leibo,et al.  The dynamics of invariant object recognition in the human visual system. , 2014, Journal of neurophysiology.

[69]  Yaoda Xu,et al.  Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations , 2017, The Journal of Neuroscience.

[70]  K. Zilles,et al.  Differentiated parietal connectivity of frontal regions for “what” and “where” memory , 2012, Brain Structure and Function.

[71]  Antonio Torralba,et al.  Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.

[72]  Susan G. Wardle,et al.  Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data , 2016, Journal of Cognitive Neuroscience.

[73]  Kalanit Grill-Spector,et al.  Task alters category representations in prefrontal but not high-level visual cortex , 2017, NeuroImage.

[74]  David D. Cox,et al.  What response properties do individual neurons need to underlie position and clutter "invariant" object recognition? , 2009, Journal of neurophysiology.

[75]  Daniel L K Yamins,et al.  Neural Mechanisms Underlying Visual Object Recognition. , 2014, Cold Spring Harbor symposia on quantitative biology.

[76]  N. Sigala,et al.  Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.

[77]  Thomas Serre,et al.  Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization , 2015, PLoS Comput. Biol..