Animal Detection Precedes Access to Scene Category

The processes underlying object recognition are fundamental for the understanding of visual perception. Humans can recognize many objects rapidly even in complex scenes, a task that still presents major challenges for computer vision systems. A common experimental demonstration of this ability is the rapid animal detection protocol, where human participants earliest responses to report the presence/absence of animals in natural scenes are observed at 250–270 ms latencies. One of the hypotheses to account for such speed is that people would not actually recognize an animal per se, but rather base their decision on global scene statistics. These global statistics (also referred to as spatial envelope or gist) have been shown to be computationally easy to process and could thus be used as a proxy for coarse object recognition. Here, using a saccadic choice task, which allows us to investigate a previously inaccessible temporal window of visual processing, we showed that animal – but not vehicle – detection clearly precedes scene categorization. This asynchrony is in addition validated by a late contextual modulation of animal detection, starting simultaneously with the availability of scene category. Interestingly, the advantage for animal over scene categorization is in opposition to the results of simulations using standard computational models. Taken together, these results challenge the idea that rapid animal detection might be based on early access of global scene statistics, and rather suggests a process based on the extraction of specific local complex features that might be hardwired in the visual system.

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

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

[3]  C. Buss,et al.  Children's Brain Development Benefits from Longer Gestation , 2011, Front. Psychology.

[4]  Olivier R. Joubert,et al.  The Time-Course of Visual Categorizations: You Spot the Animal Faster than the Bird , 2009, PloS one.

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

[6]  Karl R Gegenfurtner,et al.  Parallel visual search and rapid animal detection in natural scenes. , 2011, Journal of vision.

[7]  John M Henderson,et al.  The time course of initial scene processing for eye movement guidance in natural scene search. , 2010, Journal of vision.

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

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

[10]  A. Treisman How the deployment of attention determines what we see , 2006, Visual cognition.

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

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

[13]  Kim Mouridsen,et al.  Dopaminergic stimulation enhances confidence and accuracy in seeing rapidly presented words. , 2011, Journal of vision.

[14]  M. Potter Short-term conceptual memory for pictures. , 1976, Journal of experimental psychology. Human learning and memory.

[15]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[16]  Guillaume A. Rousselet,et al.  Parallel processing in high-level categorization of natural images , 2002, Nature Neuroscience.

[17]  J. Henderson,et al.  Does consistent scene context facilitate object perception? , 1998, Journal of experimental psychology. General.

[18]  Guillaume A. Rousselet,et al.  Processing scene context: Fast categorization and object interference , 2007, Vision Research.

[19]  J. Henderson,et al.  High-level scene perception. , 1999, Annual review of psychology.

[20]  Jan Theeuwes,et al.  Attentional and oculomotor inhibition , 2010 .

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

[22]  R VanRullen,et al.  Is it a Bird? Is it a Plane? Ultra-Rapid Visual Categorisation of Natural and Artifactual Objects , 2001, Perception.

[23]  Arnold W. M. Smeulders,et al.  A Biologically Plausible Model for Rapid Natural Scene Identification , 2009, NIPS.

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

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

[26]  Eileen Kowler,et al.  Eye movements and the perception of a clear and stable visual world. , 2008, Journal of vision.

[27]  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.

[28]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[29]  M. Fabre-Thorpe,et al.  Humans and monkeys share visual representations , 2011, Proceedings of the National Academy of Sciences.

[30]  L. Cosmides,et al.  Category-specific attention for animals reflects ancestral priorities, not expertise , 2007, Proceedings of the National Academy of Sciences.

[31]  Thomas Serre,et al.  What are the Visual Features Underlying Rapid Object Recognition? , 2011, Front. Psychology.

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

[33]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  D. B. Bender,et al.  Distribution of corticotectal cells in macaque , 2003, Experimental Brain Research.

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

[36]  G. Rousselet,et al.  Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes. , 2003, Journal of vision.

[37]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Timothée Masquelier,et al.  Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..

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

[40]  P. May The mammalian superior colliculus: laminar structure and connections. , 2006, Progress in brain research.

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

[42]  Arnold W. M. Smeulders,et al.  Brain responses strongly correlate with Weibull image statistics when processing natural images. , 2009, Journal of vision.

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

[44]  Sébastien M. Crouzet,et al.  Fast saccades toward faces: face detection in just 100 ms. , 2010, Journal of vision.

[45]  Olivier R. Joubert,et al.  How long to get to the “gist” of real-world natural scenes? , 2005 .

[46]  Guillaume A. Rousselet,et al.  Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise. , 2009, Journal of vision.

[47]  A. Smeulders,et al.  A Biologically Plausible Model for Rapid Natural Image Identi cation , 2009 .

[48]  Guillaume A. Rousselet,et al.  Early interference of context congruence on object processing in rapid visual categorization of natural scenes. , 2008, Journal of vision.

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

[50]  Lester C. Loschky,et al.  The natural/man-made distinction is made before basic-level distinctions in scene gist processing , 2010 .

[51]  J. Y. Goulermas,et al.  Multivoxel fMRI analysis of color tuning in human primary visual cortex. , 2009, Journal of vision.

[52]  Leslie G. Ungerleider,et al.  Subcortical connections of inferior temporal areas TE and TEO in macaque monkeys , 1993, The Journal of comparative neurology.

[53]  C. Koch,et al.  Visual Search and Dual Tasks Reveal Two Distinct Attentional Resources , 2004, Journal of Cognitive Neuroscience.

[54]  Guillaume A. Rousselet,et al.  Limits of Event-related Potential Differences in Tracking Object Processing Speed , 2007, Journal of Cognitive Neuroscience.

[55]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[56]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[57]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[58]  Arnaud Delorme,et al.  Key Visual Features for Rapid Categorization of Animals in Natural Scenes , 2010, Front. Psychology.

[59]  R. VanRullen Binding hardwired versus on-demand feature conjunctions , 2009 .

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

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