Hysteresis in the dynamic perception of scenes and objects.

Scenes and objects are effortlessly processed and integrated by the human visual system. Given the distinct neural and behavioral substrates of scene and object processing, it is likely that individuals sometimes preferentially rely on one process or the other when viewing canonical "scene" or "object" stimuli. This would allow the visual system to maximize the specific benefits of these 2 types of processing. It is less obvious which of these modes of perception would be invoked during naturalistic visual transition between a focused view of a single object and an expansive view of an entire scene, particularly at intermediate views that may not be assigned readily to either stimulus category. In the current study, we asked observers to report their online perception of such dynamic image sequences, which zoomed and panned between a canonical view of a single object and an entire scene. We found a large and consistent effect of prior perception, or hysteresis, on the classification of the sequence: observers classified the sequence as an object for several seconds longer if the trial started at the object view and zoomed out, whereas scenes were perceived for longer on trials beginning with a scene view. This hysteresis effect resisted several manipulations of the movie stimulus and of the task performed, but hinged on the perceptual history built by unidirectional progression through the image sequence. Multiple experiments confirmed that this hysteresis effect was not purely decisional and was more prominent for transitions between corresponding objects and scenes than between other high-level stimulus classes. This finding suggests that the competitive mechanisms underlying hysteresis may be especially prominent in the perception of objects and scenes. We propose that hysteresis aids in disambiguating perception during naturalistic visual transitions, which may facilitate a dynamic balance between scene and object processing to enhance processing efficiency.

[1]  N. Kanwisher,et al.  The lateral occipital complex and its role in object recognition , 2001, Vision Research.

[2]  L. Summers,et al.  Hysteresis and the European Unemployment Problem , 1986, NBER Macroeconomics Annual.

[3]  Tomaso Poggio,et al.  Models of object recognition , 2000, Nature Neuroscience.

[4]  A. Oliva,et al.  Segmentation of objects from backgrounds in visual search tasks , 2002, Vision Research.

[5]  B. Julesz.  Cooperative phenomena in binocular depth perception. , 1974, American scientist.

[6]  A. Treisman,et al.  Perception of objects in natural scenes: is it really attention free? , 2005, Journal of experimental psychology. Human perception and performance.

[7]  G. Bertotti.  Hysteresis in Magnetism: For Physicists, Materials Scientists, and Engineers , 1998 .

[8]  Ronald A. Rensink,et al.  TO SEE OR NOT TO SEE: The Need for Attention to Perceive Changes in Scenes , 1997 .

[9]  Karl J. Friston,et al.  The Neural Structures Expressing Perceptual Hysteresis in Visual Letter Recognition , 2002, Neuron.

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

[11]  A. Oliva,et al.  Person, place, and past influence eye movements during visual search , 2010 .

[12]  John M. Henderson,et al.  Transsaccadic Memory and Integration During Real-World Object Perception , 1997 .

[13]  M J Tarr,et al.  Is human object recognition better described by geon structural descriptions or by multiple views? Comment on Biederman and Gerhardstein (1993). , 1995, Journal of experimental psychology. Human perception and performance.

[14]  Nancy Kanwisher,et al.  A cortical representation of the local visual environment , 1998, Nature.

[15]  James T Todd,et al.  The visual perception of 3-D shape from multiple cues: Are observers capable of perceiving metric structure? , 2003, Perception & psychophysics.

[16]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  I. Biederman,et al.  Recognizing depth-rotated objects: evidence and conditions for three-dimensional viewpoint invariance. , 1993, Journal of experimental psychology. Human perception and performance.

[18]  D. Jiles,et al.  Theory of ferromagnetic hysteresis , 1986 .

[19]  A. Friedman,et al.  Optimal design for heat conduction problems with hysteresis , 1989 .

[20]  J Wilson,et al.  Spatial Frequency and Selective Attention to Local and Global Information , 1987, Perception.

[21]  J. Todd.  Review TRENDS in Cognitive Sciences Vol.8 No.3 March 2004 The visual perception of 3D shape q , 2022 .

[22]  B. Julesz,et al.  Extension of Panum's fusional area in binocularly stabilized vision. , 1967, Journal of the Optical Society of America.

[23]  M. Kutas,et al.  An electrophysiological study of scene effects on object identification. , 2003, Brain research. Cognitive brain research.

[24]  J. Henderson,et al.  Accurate visual memory for previously attended objects in natural scenes , 2002 .

[25]  H H Bülthoff,et al.  How are three-dimensional objects represented in the brain? , 1994, Cerebral cortex.

[26]  Greg O. Horne,et al.  Controlling low-level image properties: The SHINE toolbox , 2010, Behavior research methods.

[27]  M. Bar.  Visual objects in context , 2004, Nature Reviews Neuroscience.

[28]  Jodi L. Davenport.  Consistency effects between objects in scenes , 2007, Memory & cognition.

[29]  Carson C. Chow,et al.  A Spiking Neuron Model for Binocular Rivalry , 2004, Journal of Computational Neuroscience.

[30]  Jennifer K. E. Steeves,et al.  TMS to the Lateral Occipital Cortex Disrupts Object Processing but Facilitates Scene Processing , 2011, Journal of Cognitive Neuroscience.

[31]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[32]  Gunther W. Balz,et al.  Bistability and hysteresis in the organization of apparent motion patterns. , 1993, Journal of experimental psychology. Human perception and performance.

[33]  Howard S Hock,et al.  Dynamical vs. judgmental comparison: hysteresis effects in motion perception. , 2005, Spatial vision.

[34]  Russell A. Epstein.  Parahippocampal and retrosplenial contributions to human spatial navigation , 2008, Trends in Cognitive Sciences.

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

[36]  Guillaume A. Rousselet,et al.  Processing of one, two or four natural scenes in humans: the limits of parallelism , 2004, Vision Research.

[37]  C. Koch,et al.  Competition and selection during visual processing of natural scenes and objects. , 2003, Journal of vision.

[38]  Klaus R. Scherer,et al.  The perception of changing emotion expressions , 2012, Cognition & emotion.

[39]  Michelle R. Greene,et al.  Recognition of natural scenes from global properties: Seeing the forest without representing the trees , 2009, Cognitive Psychology.

[40]  Z Kourtzi,et al.  Representation of Perceived Object Shape by the Human Lateral Occipital Complex , 2001, Science.

[41]  P. Perona,et al.  Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[42]  David C. Burr,et al.  Smooth and sampled motion , 1986, Vision Research.

[43]  A. Oliva,et al.  Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli , 1997, Cognitive Psychology.

[44]  D. Hubel,et al.  Segregation of form, color, movement, and depth: anatomy, physiology, and perception. , 1988, Science.

[45]  A. Oliva,et al.  From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition , 1994 .

[46]  A. Oliva,et al.  Diagnostic Colors Mediate Scene Recognition , 2000, Cognitive Psychology.

[47]  Hongzhi You,et al.  The neural dynamics for hysteresis in visual perception , 2011, Neurocomputing.

[48]  I. Biederman,et al.  Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. , 1993 .

[49]  Jitendra Malik,et al.  When is scene identification just texture recognition? , 2004, Vision Research.

[50]  John Ian Ferrell,et al.  Detection of multi-stability , 2004 .

[51]  Heinrich H. Bülthoff,et al.  Categorization of natural scenes: local vs. global information , 2006, APGV '06.

[52]  Philippe G Schyns,et al.  Diagnostic recognition: task constraints, object information, and their interactions , 1998, Cognition.

[53]  Charles A. Collin,et al.  Subordinate-level categorization relies on high spatial frequencies to a greater degree than basic-level categorization , 2005, Perception & psychophysics.

[54]  G. Brelstaff,et al.  Is the Richness of Our Visual World an Illusion? Transsaccadic Memory for Complex Scenes , 1995, Perception.

[55]  Michelle R. Greene,et al.  PSYCHOLOGICAL SCIENCE Research Article The Briefest of Glances The Time Course of Natural Scene Understanding , 2022 .

[56]  Antonio Torralba,et al.  Depth Estimation from Image Structure , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[57]  A. Yuille,et al.  Object perception as Bayesian inference. , 2004, Annual review of psychology.

[58]  Andrea De Cesarei,et al.  Global and local vision in natural scene identification , 2011, Psychonomic bulletin & review.

[59]  M. J. Tarr,et al.  COMMENT ON: IS HUMAN OBJECT RECOGNITION BETTER DESCRIBED BY GEON STRUCTURAL DESCRIPTIONS OR BY MULTIPLE VIEWS ? BY BARTRAM D.J. , 1995 .

[60]  R. Catrambone,et al.  Proceedings of the 32nd Annual Conference of the Cognitive Science Society , 2010 .

[61]  O. H. Schmitt,et al.  A thermionic trigger , 1938 .

[62]  Robert L. Goldstone.  The role of similarity in categorization: providing a groundwork , 1994, Cognition.

[63]  tephen E. Palmer.  The effects of contextual scenes on the identification of objects , 1975, Memory & cognition.

[64]  Isabel Gauthier,et al.  Three-dimensional object recognition is viewpoint dependent , 1998, Nature Neuroscience.

[65]  Jeounghoon Kim,et al.  Hysteresis effects in stereopsis and binocular rivalry , 2008, Vision Research.

[66]  R. Sekuler,et al.  Hysteresis in the perception of motion direction as evidence for neural cooperativity , 1986, Nature.

[67]  A. Torralba,et al.  The role of context in object recognition , 2007, Trends in Cognitive Sciences.

[68]  S. Edelman,et al.  Differential Processing of Objects under Various Viewing Conditions in the Human Lateral Occipital Complex , 1999, Neuron.

[69]  Alexander Maier,et al.  Perception of Temporally Interleaved Ambiguous Patterns , 2003, Current Biology.

[70]  H. Bülthoff,et al.  Representation of the perceived 3-D object shape in the human lateral occipital complex. , 2003, Cerebral cortex.

[71]  Joel Pearson,et al.  Sensory memory for ambiguous vision , 2008, Trends in Cognitive Sciences.

[72]  J R Lishman,et al.  Role of coarse and fine spatial information in face and object processing. , 1996, Journal of experimental psychology. Human perception and performance.

[73]  David A. Leopold,et al.  Stable perception of visually ambiguous patterns , 2002, Nature Neuroscience.

[74]  H. Pashler.  Dual-task interference in simple tasks: data and theory. , 1994, Psychological bulletin.

[75]  Russell A. Epstein,et al.  Differential parahippocampal and retrosplenial involvement in three types of visual scene recognition. , 2006, Cerebral cortex.

[76]  Michael J. Tarr.  Is human object recognition better described by geon structural description or by multiple views , 1995 .

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

[78]  Jodi L. Davenport,et al.  Scene Consistency in Object and Background Perception , 2004, Psychological science.

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

[80]  George A. Alvarez,et al.  Natural-Scene Perception Requires Attention , 2011, Psychological science.

[81]  Andreas Bartels,et al.  A Transcription Factor Response Element for Gene Expression During , 2022 .

[82]  John Ross,et al.  Visual processing of motion , 1986, Trends in Neurosciences.

[83]  Soojin Park,et al.  Different roles of the parahippocampal place area (PPA) and retrosplenial cortex (RSC) in panoramic scene perception , 2009, NeuroImage.

[84]  Daniel D. Dilks,et al.  The Occipital Place Area Is Causally and Selectively Involved in Scene Perception , 2013, The Journal of Neuroscience.

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

[86]  DH Hubel,et al.  Psychophysical evidence for separate channels for the perception of form, color, movement, and depth , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[88]  Shinsuke Shimojo,et al.  Visual surface representation: a critical link between lower-level and higher level vision , 1995 .

[89]  Elizabeth S. Spelke,et al.  Object perception , 1993 .

[90]  I. Gauthier,et al.  Visual object understanding , 2004, Nature Reviews Neuroscience.

[91]  K. Nakayama,et al.  Temporal and spatial characteristics of the upper displacement limit for motion in random dots , 1984, Vision Research.