Parts and Wholes in Scene Processing

Abstract During natural vision, our brains are constantly exposed to complex, but regularly structured, environments. Real-world scenes are defined by typical part–whole relationships, where the meaning of the whole scene emerges from configurations of localized information present in individual parts of the scene. Such typical part–whole relationships suggest that information from individual scene parts is not processed independently, but that there are mutual influences between the parts and the whole during scene analysis. Here, we review recent research that used a straightforward, but effective approach to study such mutual influences: By dissecting scenes into multiple arbitrary pieces, these studies provide new insights into how the processing of whole scenes is shaped by their constituent parts and, conversely, how the processing of individual parts is determined by their role within the whole scene. We highlight three facets of this research: First, we discuss studies demonstrating that the spatial configuration of multiple scene parts has a profound impact on the neural processing of the whole scene. Second, we review work showing that cortical responses to individual scene parts are shaped by the context in which these parts typically appear within the environment. Third, we discuss studies demonstrating that missing scene parts are interpolated from the surrounding scene context. Bridging these findings, we argue that efficient scene processing relies on an active use of the scene's part–whole structure, where the visual brain matches scene inputs with internal models of what the world should look like.

[1]  ChrisD . Frith,et al.  Precision and the Bayesian brain , 2021, Current Biology.

[2]  M. Sereno,et al.  Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity , 2021, The Journal of Neuroscience.

[3]  Talia Konkle,et al.  Systematic transition from boundary extension to contraction along an object-to-scene continuum , 2021, Journal of vision.

[4]  L. Papeo Twos in human visual perception , 2020, Cortex.

[5]  Monica S Castelhano,et al.  Rethinking Space: A Review of Perception, Attention, and Memory in Scene Processing. , 2020, Annual review of vision science.

[6]  K. Rauss,et al.  Anatomic and functional asymmetries interactively shape human early visual cortex responses , 2020, Journal of vision.

[7]  Caroline E. Robertson,et al.  A network linking perception and memory systems in posterior cerebral cortex , 2020, bioRxiv.

[8]  Radoslaw Martin Cichy,et al.  Real-world structure facilitates the rapid emergence of scene category information in visual brain signals , 2020, bioRxiv.

[9]  Jeongho Park,et al.  Coding of Navigational Distance and Functional Constraint of Boundaries in the Human Scene-Selective Cortex , 2020, The Journal of Neuroscience.

[10]  M. Bar,et al.  Overarching States of Mind , 2020, Trends in Cognitive Sciences.

[11]  Radoslaw Martin Cichy,et al.  Rapid contextualization of fragmented scene information in the human visual system , 2020, NeuroImage.

[12]  Wilma A. Bainbridge,et al.  Boundaries Extend and Contract in Scene Memory Depending on Image Properties , 2019, Current Biology.

[13]  Galit Yovel,et al.  The Functional Organization of High-Level Visual Cortex Determines the Representation of Complex Visual Stimuli , 2019, The Journal of Neuroscience.

[14]  R. Tootell,et al.  Asymmetries in Global Perception Are Represented in Near- versus Far-Preferring Clusters in Human Visual Cortex , 2019, The Journal of Neuroscience.

[15]  P. Kok,et al.  The Perceptual Prediction Paradox , 2019, Trends in Cognitive Sciences.

[16]  Lars Muckli,et al.  Scene Representations Conveyed by Cortical Feedback to Early Visual Cortex Can Be Described by Line Drawings , 2019, The Journal of Neuroscience.

[17]  Russell A. Epstein,et al.  Scene Perception in the Human Brain. , 2019, Annual review of vision science.

[18]  Merim Bilalić,et al.  Parsing rooms: the role of the PPA and RSC in perceiving object relations and spatial layout , 2019, Brain Structure and Function.

[19]  Radoslaw Martin Cichy,et al.  A neural mechanism for contextualizing fragmented inputs during naturalistic vision , 2019, eLife.

[20]  Radoslaw Martin Cichy,et al.  Object Vision in a Structured World , 2019, Trends in Cognitive Sciences.

[21]  Radoslaw Martin Cichy,et al.  Cortical sensitivity to natural scene structure , 2019, bioRxiv.

[22]  M. Võ,et al.  Reading scenes: how scene grammar guides attention and aids perception in real-world environments. , 2019, Current opinion in psychology.

[23]  Nikolaus Kriegeskorte,et al.  Rapid Invariant Encoding of Scene Layout in Human OPA , 2019, Neuron.

[24]  J. Dubois,et al.  Imaging object-scene relations processing in visible and invisible natural scenes , 2019, Scientific Reports.

[25]  Jack L. Gallant,et al.  Human Scene-Selective Areas Represent 3D Configurations of Surfaces , 2019, Neuron.

[26]  Georg B. Keller,et al.  Predictive Processing: A Canonical Cortical Computation , 2018, Neuron.

[27]  F. D. Lange,et al.  How Do Expectations Shape Perception? , 2018, Trends in Cognitive Sciences.

[28]  Jason Rajsic,et al.  Discriminating scene categories from brain activity within 100 milliseconds , 2018, Cortex.

[29]  Radoslaw Martin Cichy,et al.  Typical visual-field locations enhance processing in object-selective channels of human occipital cortex. , 2018, Journal of neurophysiology.

[30]  Russell A. Epstein,et al.  Computational mechanisms underlying cortical responses to the affordance properties of visual scenes , 2017, bioRxiv.

[31]  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.

[32]  Marius V. Peelen,et al.  Transformation from independent to integrative coding of multi-object arrangements in human visual cortex , 2017, NeuroImage.

[33]  Russell A. Epstein,et al.  Coding of navigational affordances in the human visual system , 2017, Proceedings of the National Academy of Sciences.

[34]  Chris I Baker,et al.  Contributions of low- and high-level properties to neural processing of visual scenes in the human brain , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[35]  Christopher Baldassano,et al.  Human‐Object Interactions Are More than the Sum of Their Parts , 2016, Cerebral cortex.

[36]  Dimitrios Pantazis,et al.  Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks , 2015, NeuroImage.

[37]  Geraint Rees,et al.  Perception and Processing of Faces in the Human Brain Is Tuned to Typical Feature Locations , 2016, The Journal of Neuroscience.

[38]  Li Fei-Fei,et al.  Two Distinct Scene-Processing Networks Connecting Vision and Memory , 2016, eNeuro.

[39]  Dwight J Kravitz,et al.  The Temporal Dynamics of Scene Processing: A Multifaceted EEG Investigation , 2016, eNeuro.

[40]  C. Connor,et al.  Representation of Gravity-Aligned Scene Structure in Ventral Pathway Visual Cortex , 2016, Current Biology.

[41]  James W Tanaka,et al.  The “Parts and Wholes” of Face Recognition: A Review of the Literature , 2016, Quarterly journal of experimental psychology.

[42]  Galit Yovel,et al.  Bodies are Represented as Wholes Rather Than Their Sum of Parts in the Occipital-Temporal Cortex. , 2014, Cerebral cortex.

[43]  J. Wagemans,et al.  Brain-decoding fMRI reveals how wholes relate to the sum of parts , 2015, Cortex.

[44]  Lucy S. Petro,et al.  Contextual Feedback to Superficial Layers of V1 , 2015, Current Biology.

[45]  Dwight J. Kravitz,et al.  A Retinotopic Basis for the Division of High-Level Scene Processing between Lateral and Ventral Human Occipitotemporal Cortex , 2015, The Journal of Neuroscience.

[46]  Damien J. Mannion Sensitivity to the visual field origin of natural image patches in human low-level visual cortex , 2015, PeerJ.

[47]  N. Kriegeskorte,et al.  Faciotopy—A face-feature map with face-like topology in the human occipital face area , 2015, Cortex.

[48]  Aude Oliva,et al.  Parametric Coding of the Size and Clutter of Natural Scenes in the Human Brain. , 2014, Cerebral cortex.

[49]  Russell A. Epstein Neural Systems for Visual Scene Recognition , 2014 .

[50]  Tom Hartley,et al.  Patterns of response to visual scenes are linked to the low-level properties of the image , 2014, NeuroImage.

[51]  Marius V Peelen,et al.  Object grouping based on real-world regularities facilitates perception by reducing competitive interactions in visual cortex , 2014, Proceedings of the National Academy of Sciences.

[52]  J. Wolfe,et al.  Differential Electrophysiological Signatures of Semantic and Syntactic Scene Processing , 2013, Psychological science.

[53]  A. Clark Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.

[54]  Fei Guo,et al.  Neural Representations of Contextual Guidance in Visual Search of Real-World Scenes , 2013, The Journal of Neuroscience.

[55]  Johan Wagemans,et al.  The distributed representation of random and meaningful object pairs in human occipitotemporal cortex: The weighted average as a general rule , 2013, NeuroImage.

[56]  Dwight J. Kravitz,et al.  Deconstructing visual scenes in cortex: gradients of object and spatial layout information. , 2013, Cerebral cortex.

[57]  Sang Ah Lee,et al.  Core systems of geometry in animal minds , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[58]  Janneke F. M. Jehee,et al.  Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex , 2012, Neuron.

[59]  Russell A. Epstein,et al.  Constructing scenes from objects in human occipitotemporal cortex , 2011, Nature Neuroscience.

[60]  I. Biederman,et al.  Neural encoding of relative position. , 2011, Journal of experimental psychology. Human perception and performance.

[61]  Dwight J. Kravitz,et al.  Real-World Scene Representations in High-Level Visual Cortex: It's the Spaces More Than the Places , 2011, The Journal of Neuroscience.

[62]  Li Fei-Fei,et al.  Simple line drawings suffice for functional MRI decoding of natural scene categories , 2011, Proceedings of the National Academy of Sciences.

[63]  Rajesh P. N. Rao,et al.  Predictive Coding , 2019, A Blueprint for the Hard Problem of Consciousness.

[64]  Dale Purves,et al.  Understanding vision in wholly empirical terms , 2011, Proceedings of the National Academy of Sciences.

[65]  Jiye G. Kim,et al.  Where do objects become scenes? , 2011, Cerebral cortex.

[66]  Fraser W. Smith,et al.  Nonstimulated early visual areas carry information about surrounding context , 2010, Proceedings of the National Academy of Sciences.

[67]  Markus Lappe,et al.  The contribution of scene context on change detection performance , 2010, Vision Research.

[68]  Damien J. Mannion,et al.  Orientation anisotropies in human visual cortex. , 2010, Journal of neurophysiology.

[69]  L. Deouell,et al.  ERP evidence for context congruity effects during simultaneous object–scene processing , 2010, Neuropsychologia.

[70]  Dwight J. Kravitz,et al.  Cortical representations of bodies and faces are strongest in their commonly experienced configurations , 2010, Nature Neuroscience.

[71]  Jia Liu,et al.  Perception of Face Parts and Face Configurations: An fMRI Study , 2010, Journal of Cognitive Neuroscience.

[72]  Dirk B. Walther,et al.  Natural Scene Categories Revealed in Distributed Patterns of Activity in the Human Brain , 2009, The Journal of Neuroscience.

[73]  Russell A. Epstein,et al.  Decoding the Representation of Multiple Simultaneous Objects in Human Occipitotemporal Cortex , 2009, Current Biology.

[74]  W. Geisler Visual perception and the statistical properties of natural scenes. , 2008, Annual review of psychology.

[75]  Tai Sing Lee,et al.  Contextual Influences in Visual Processing , 2008 .

[76]  H. Intraub,et al.  Beyond the Edges of a View: Boundary Extension in Human Scene-Selective Visual Cortex , 2007, Neuron.

[77]  A. Yuille,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Vision as Bayesian inference: analysis by synthesis? , 2022 .

[78]  H. Komatsu The neural mechanisms of perceptual filling-in , 2006, Nature Reviews Neuroscience.

[79]  H. Wilson,et al.  fMRI evidence for the neural representation of faces , 2005, Nature Neuroscience.

[80]  Konrad Paul Kording,et al.  Processing of complex stimuli and natural scenes in the visual cortex , 2004, Current Opinion in Neurobiology.

[81]  Dale Purves,et al.  A statistical explanation of visual space , 2003, Nature Neuroscience.

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

[83]  Kazuhiko Yokosawa,et al.  Does disruption of a scene impair change detection? , 2003, Journal of vision.

[84]  D. Maurer,et al.  The many faces of configural processing , 2002, Trends in Cognitive Sciences.

[85]  Peter De Weerd,et al.  Responses of cells in monkey visual cortex during perceptual filling-in of an artificial scotoma , 1995, Nature.

[86]  H Intraub,et al.  Looking at pictures but remembering scenes. , 1992, Journal of experimental psychology. Learning, memory, and cognition.

[87]  F. Previc Functional specialization in the lower and upper visual fields in humans: Its ecological origins and neurophysiological implications , 1990, Behavioral and Brain Sciences.

[88]  H. Intraub,et al.  Wide-angle memories of close-up scenes. , 1989, Journal of experimental psychology. Learning, memory, and cognition.

[89]  J. Mandler Stories, Scripts, and Scenes: Aspects of Schema Theory , 1984 .

[90]  I. Biederman,et al.  Scene perception: Detecting and judging objects undergoing relational violations , 1982, Cognitive Psychology.

[91]  I. Biederman,et al.  On the information extracted from a glance at a scene. , 1974, Journal of experimental psychology.

[92]  I. Biederman,et al.  Searching for objects in real-world scences. , 1973, Journal of experimental psychology.

[93]  I. Biederman Perceiving Real-World Scenes , 1972, Science.