The resilience of object predictions: Early recognition across viewpoints and exemplars

Recognition of everyday objects can be facilitated by top-down predictions. We have proposed that these predictions are derived from rudimentary image information, or gist, extracted rapidly from the low spatial frequencies (LSFs) (Bar Journal of Cognitive Neuroscience 15: 600–609, 2003). Because of the coarse nature of LSF representations, we hypothesized here that such predictions can accommodate changes in viewpoint as well as facilitate the recognition of visually similar objects. In a repetition-priming task, we indeed observed significant facilitation of target recognition that was primed by LSF objects across moderate viewpoint changes, as well as across visually similar exemplars. These results suggest that the LSF representations are specific enough to activate accurate predictions, yet flexible enough to overcome small changes in visual appearance. Such gist representations facilitate object recognition by accommodating changes in visual appearance due to viewing conditions, and help generalize from familiar to novel exemplars.

[1]  Michael L. Mack,et al.  Modeling categorization of scenes containing consistent versus inconsistent objects. , 2010, Journal of vision.

[2]  I. Gauthier,et al.  Brain areas engaged during visual judgments by involuntary access to novel semantic information , 2004, Vision Research.

[3]  P. Mcmullen,et al.  Effects of orientation on the identification of rotated objects depend on the level of identity. , 1998, Journal of experimental psychology. Human perception and performance.

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

[5]  Janice E. Murray,et al.  Is entry-level recognition viewpoint invariant or viewpoint dependent? , 1998 .

[6]  Timothy F. Brady,et al.  Conceptual Distinctiveness Supports Detailed Visual Long-term Memory for Real-world Objects the Fidelity of Long-term Memory for Visual Information , 2022 .

[7]  Padraic Monaghan,et al.  Proceedings of the 23rd annual conference of the cognitive science society , 2001 .

[8]  M. Tarr,et al.  Testing conditions for viewpoint invariance in object recognition. , 1997, Journal of experimental psychology. Human perception and performance.

[9]  Wilma Koutstaal,et al.  Neural mechanisms of visual object priming: evidence for perceptual and semantic distinctions in fusiform cortex , 2003, NeuroImage.

[10]  A. Oliva,et al.  Dr. Angry and Mr. Smile: when categorization flexibly modifies the perception of faces in rapid visual presentations , 1999, Cognition.

[11]  P. Hagoort,et al.  The suppression of repetition enhancement: A review of fMRI studies , 2013, Neuropsychologia.

[12]  P. Schyns,et al.  A Natural Bias For the Basic Level ? , 2015 .

[13]  Moshe Bar,et al.  Visual predictions in the orbitofrontal cortex rely on associative content. , 2014, Cerebral cortex.

[14]  K. Grill-Spector,et al.  Repetition and the brain: neural models of stimulus-specific effects , 2006, Trends in Cognitive Sciences.

[15]  Luca Passamonti,et al.  Changes in “Top-Down” Connectivity Underlie Repetition Suppression in the Ventral Visual Pathway , 2011, The Journal of Neuroscience.

[16]  Sid Kouider,et al.  How Spatial Frequencies and Visual Awareness Interact During Face Processing , 2010, Psychological science.

[17]  M. Potter.  Meaning in visual search. , 1975, Science.

[18]  I. Biederman,et al.  Evidence for Complete Translational and Reflectional Invariance in Visual Object Priming , 1991, Perception.

[19]  Paul E. Dux,et al.  Orientation-invariant object recognition: evidence from repetition blindness , 2005, Cognition.

[20]  Wayne D. Gray,et al.  Basic objects in natural categories , 1976, Cognitive Psychology.

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

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

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

[24]  P. Schyns,et al.  Show Me the Features! Understanding Recognition From the Use of Visual Information , 2002, Psychological science.

[25]  Jie Tian,et al.  Effective connectivities of cortical regions for top-down face processing: A Dynamic Causal Modeling study , 2010, Brain Research.

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

[27]  S. Thorpe,et al.  A Limit to the Speed of Processing in Ultra-Rapid Visual Categorization of Novel Natural Scenes , 2001, Journal of Cognitive Neuroscience.

[28]  Abel G. Oliva,et al.  Gist of a scene , 2005 .

[29]  Bruno G. Breitmeyer,et al.  Simple reaction time as a measure of the temporal response properties of transient and sustained channels , 1975, Vision Research.

[30]  James B. Rowe,et al.  Different Neural Mechanisms within Occipitotemporal Cortex Underlie Repetition Suppression across Same and Different-Size Faces , 2012, Cerebral cortex.

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

[32]  John H. R. Maunsell,et al.  Mixed parvocellular and magnocellular geniculate signals in visual area V4 , 1992, Nature.

[33]  M. Bar,et al.  The effects of priming on frontal-temporal communication , 2008, Proceedings of the National Academy of Sciences.

[34]  R. Henson,et al.  Multiple levels of visual object constancy revealed by event-related fMRI of repetition priming , 2002, Nature Neuroscience.

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

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

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

[38]  E. D. Burgund,et al.  Neural adaptation across viewpoint and exemplar in fusiform cortex , 2012, Brain and Cognition.

[39]  R Shapley,et al.  Visual sensitivity and parallel retinocortical channels. , 1990, Annual review of psychology.

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

[41]  Mark D'Esposito,et al.  The Prefrontal Cortex Modulates Category Selectivity in Human Extrastriate Cortex , 2011, Journal of Cognitive Neuroscience.

[42]  Stephen M. Kosslyn,et al.  Pictures and names: Making the connection , 1984, Cognitive Psychology.

[43]  Jonathan Grainger,et al.  Unconscious semantic priming from pictures , 1999, Cognition.

[44]  U. Neisser.  Cognitive Psychology: Classic Edition , 1967 .

[45]  Gordon L. Shulman,et al.  Reaction times to different spatial frequencies as a function of detectability , 1986, Vision Research.

[46]  D. Schacter,et al.  Perceptual specificity in visual object priming: functional magnetic resonance imaging evidence for a laterality difference in fusiform cortex , 2001, Neuropsychologia.

[47]  R. Mayeux,et al.  Simple reaction time as a measure of global attention in Alzheimer's disease , 1995, Journal of the International Neuropsychological Society.

[48]  John H. R. Maunsell,et al.  How parallel are the primate visual pathways? , 1993, Annual review of neuroscience.