Stereo Viewing Modulates Three-Dimensional Shape Processing During Object Recognition: A High-Density ERP Study

The role of stereo disparity in the recognition of 3-dimensional (3D) object shape remains an unresolved issue for theoretical models of the human visual system. We examined this issue using high-density (128 channel) recordings of event-related potentials (ERPs). A recognition memory task was used in which observers were trained to recognize a subset of complex, multipart, 3D novel objects under conditions of either (bi-) monocular or stereo viewing. In a subsequent test phase they discriminated previously trained targets from untrained distractor objects that shared either local parts, 3D spatial configuration, or neither dimension, across both previously seen and novel viewpoints. The behavioral data showed a stereo advantage for target recognition at untrained viewpoints. ERPs showed early differential amplitude modulations to shape similarity defined by local part structure and global 3D spatial configuration. This occurred initially during an N1 component around 145–190 ms poststimulus onset, and then subsequently during an N2/P3 component around 260–385 ms poststimulus onset. For mono viewing, amplitude modulation during the N1 was greatest between targets and distracters with different local parts for trained views only. For stereo viewing, amplitude modulation during the N2/P3 was greatest between targets and distracters with different global 3D spatial configurations and generalized across trained and untrained views. The results show that image classification is modulated by stereo information about the local part, and global 3D spatial configuration of object shape. The findings challenge current theoretical models that do not attribute functional significance to stereo input during the computation of 3D object shape.

[1]  William G. Hayward,et al.  A stereo disadvantage for recognizing rotated familiar objects , 2009, Psychonomic bulletin & review.

[2]  Darren Burke,et al.  Combining disparate views of objects: Viewpoint costs are reduced by stereopsis , 2005 .

[3]  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.

[4]  Martin Arguin,et al.  Independent Processing of Parts and of Their Spatial Organization in Complex Visual Objects , 2004, Psychological science.

[5]  Alex M. Andrew,et al.  Object Recognition in Man, Monkey, and Machine , 2000 .

[6]  D. Navon.  Forest before trees: The precedence of global features in visual perception , 1977, Cognitive Psychology.

[7]  E. Leek.  The Analysis of Orientation-Dependent Time Costs in Visual Recognition , 1998, Perception.

[8]  C J Erkelens,et al.  Depth cue combination in spontaneous eye movements. , 2010, Journal of vision.

[9]  Christoph M. Michel,et al.  Spatiotemporal Analysis of Multichannel EEG: CARTOOL , 2011, Comput. Intell. Neurosci..

[10]  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.

[11]  D. Guthrie,et al.  Significance testing of difference potentials. , 1991, Psychophysiology.

[12]  Giovanni d'Avossa,et al.  Impaired integration of object knowledge and visual input in a case of ventral simultanagnosia with bilateral damage to area V4 , 2012, Cognitive neuropsychology.

[13]  Karl F. Stock,et al.  A COMPUTATIONAL MODEL , 2011 .

[14]  A. Young,et al.  Transfer between two- and three-dimensional representations of faces , 2006 .

[15]  Hiroshi Ban,et al.  fMRI Analysis-by-Synthesis Reveals a Dorsal Hierarchy That Extracts Surface Slant , 2015, The Journal of Neuroscience.

[16]  Stephen J. Johnston,et al.  A polarity effect in misoriented object recognition: The role of polar features in the computation of orientation-invariant shape representations , 2006 .

[17]  Darren Burke,et al.  Are face representations viewpoint dependent? A stereo advantage for generalising across different views of faces , 2007, Vision Research.

[18]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

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

[20]  Shimon Edelman,et al.  Viewpoint-specific Representations in Three-dimensional Object Recognition , 1990 .

[21]  M. Behrmann,et al.  Independent representation of parts and the relations between them: evidence from integrative agnosia. , 2006, Journal of experimental psychology. Human perception and performance.

[22]  Zygmunt Pizlo,et al.  A computational model that recovers the 3D shape of an object from a single 2D retinal representation , 2009, Vision Research.

[23]  Zoe J. Oliver,et al.  Early differential sensitivity of evoked-potentials to local and global shape during the perception of three-dimensional objects , 2016, Neuropsychologia.

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

[25]  William G. Hayward,et al.  Stereo Disparity Facilitates View Generalization during Shape Recognition for Solid Multipart Objects , 2015, Quarterly journal of experimental psychology.

[26]  Martin Arguin,et al.  Orientation invariance in visual object priming depends on prime—target asynchrony , 2003, Perception & psychophysics.

[27]  Mark Wexler,et al.  Depth Affects Where We Look , 2008, Current Biology.

[28]  J. Hummel,et al.  An architecture for rapid, hierarchical structural description , 1996 .

[29]  Zygmunt Pizlo,et al.  3D Shape - Its Unique Place in Visual Perception , 2008 .

[30]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[31]  Monica Baciu,et al.  Cerebral regions and hemispheric specialization for processing spatial frequencies during natural scene recognition. An event-related fMRI study , 2004, NeuroImage.

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

[33]  G. Mangun,et al.  Neural Mechanisms of Global and Local Processing: A Combined PET and ERP Study , 1998, Journal of Cognitive Neuroscience.

[34]  J. Gallant,et al.  Identifying natural images from human brain activity , 2008, Nature.

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

[36]  Quoc C Vuong,et al.  A stereo advantage in generalizing over changes in viewpoint on object recognition tasks. , 2010, Perception & psychophysics.

[37]  Michèle Fabre-Thorpe,et al.  The Characteristics and Limits of Rapid Visual Categorization , 2011, Front. Psychology.

[38]  J. Koenderink,et al.  Surface perception in pictures , 1992, Perception & psychophysics.

[39]  Ranxiao Frances Wang,et al.  Object recognition is mediated by extraretinal information , 2002, Perception & psychophysics.

[40]  D. Foster,et al.  Recognizing novel three–dimensional objects by summing signals from parts and views , 2002, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[41]  H. Bülthoff,et al.  3D shape perception from combined depth cues in human visual cortex , 2005, Nature Neuroscience.

[42]  H H Bülthoff,et al.  Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

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

[45]  James L. McClelland,et al.  Information integration in perception and communication , 1996 .

[46]  Zygmunt Pizlo,et al.  Depth Cues Versus the Simplicity Principle in 3D Shape Perception , 2011, Top. Cogn. Sci..

[47]  Richard G. Kurial,et al.  Representation and recognition , 1990 .

[48]  J. Andrade-Cetto.  Object Recognition , 2003 .

[49]  Lina I. Davitt,et al.  Implicit encoding of extrinsic object properties in stored representations mediating recognition: Evidence from shadow-specific repetition priming , 2015, Vision Research.

[50]  R. Kimchi,et al.  What does visual agnosia tell us about perceptual organization and its relationship to object perception? , 2003, Journal of experimental psychology. Human perception and performance.

[51]  D. Lehmann,et al.  Reference-free identification of components of checkerboard-evoked multichannel potential fields. , 1980, Electroencephalography and clinical neurophysiology.

[52]  D. Reisberg.  The Oxford Handbook of Cognitive Psychology , 2013 .

[53]  J. Hegdé.  Time course of visual perception: Coarse-to-fine processing and beyond , 2008, Progress in Neurobiology.

[54]  Young Lim Lee,et al.  Stereo improves 3D shape discrimination even when rich monocular shape cues are available. , 2011, Journal of vision.

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

[56]  Zygmunt Pizlo,et al.  New approach to the perception of 3D shape based on veridicality, complexity, symmetry and volume , 2010, Vision Research.

[57]  Guillaume Thierry,et al.  Computational mechanisms of object constancy for visual recognition revealed by event-related potentials , 2007, Vision Research.

[58]  Zygmunt Pizlo,et al.  Binocular shape constancy from novel views: The role of a priori constraints , 2006, Perception & psychophysics.

[59]  I. Rock,et al.  A case of viewer-centered object perception , 1987, Cognitive Psychology.

[60]  S. Ullman.  Object recognition and segmentation by a fragment-based hierarchy , 2007, Trends in Cognitive Sciences.

[61]  Radoslaw Martin Cichy,et al.  Resolving human object recognition in space and time , 2014, Nature Neuroscience.

[62]  James T. Todd,et al.  The perception of surface orientation from multiple sources of optical information , 1995, Perception & psychophysics.

[63]  Paul E. Dux,et al.  Orientation Sensitivity at Different Stages of Object Processing: Evidence from Repetition Priming and Naming , 2008, PloS one.

[64]  David M. Groppe,et al.  Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review. , 2011, Psychophysiology.

[65]  E. C. Leek,et al.  Effects of stimulus orientation on the identification of common polyoriented objects , 1998 .

[66]  Irene Reppa,et al.  The structure of three-dimensional object representations in human vision: evidence from whole-part matching. , 2005, Journal of experimental psychology. Human perception and performance.

[67]  Mark V. Roberts,et al.  The Time Course of Activation of Object Shape and Shape+Colour Representations during Memory Retrieval , 2012, PloS one.

[68]  Saharon Shelah.  On Independent Representation , 2001 .

[69]  G. Humphrey,et al.  Recognizing novel views of three-dimensional objects. , 1992, Canadian journal of psychology.

[70]  Carole Peyrin,et al.  Hemispheric specialization for spatial frequency processing in the analysis of natural scenes , 2003, Brain and Cognition.

[71]  Simon J. Thorpe,et al.  Ultra-rapid object detection with saccadic eye movements: Visual processing speed revisited , 2006, Vision Research.

[72]  J Farley Norman,et al.  Stereoscopic shape discrimination is well preserved across changes in object size. , 2009, Acta psychologica.

[73]  Thomas F. Münte,et al.  The Order of Global- and Local-Level Information Processing: Electrophysiological Evidence for Parallel Perceptual Processes , 1994 .

[74]  Denis Brunet,et al.  Topographic ERP Analyses: A Step-by-Step Tutorial Review , 2008, Brain Topography.

[75]  S. Thorpe,et al.  The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.