Affective and contextual values modulate spatial frequency use in object recognition

Visual object recognition is of fundamental importance in our everyday interaction with the environment. Recent models of visual perception emphasize the role of top-down predictions facilitating object recognition via initial guesses that limit the number of object representations that need to be considered. Several results suggest that this rapid and efficient object processing relies on the early extraction and processing of low spatial frequencies (LSF). The present study aimed to investigate the SF content of visual object representations and its modulation by contextual and affective values of the perceived object during a picture-name verification task. Stimuli consisted of pictures of objects equalized in SF content and categorized as having low or high affective and contextual values. To access the SF content of stored visual representations of objects, SFs of each image were then randomly sampled on a trial-by-trial basis. Results reveal that intermediate SFs between 14 and 24 cycles per object (2.3–4 cycles per degree) are correlated with fast and accurate identification for all categories of objects. Moreover, there was a significant interaction between affective and contextual values over the SFs correlating with fast recognition. These results suggest that affective and contextual values of a visual object modulate the SF content of its internal representation, thus highlighting the flexibility of the visual recognition system.

[1]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[2]  M. Bar,et al.  Magnocellular Projections as the Trigger of Top-Down Facilitation in Recognition , 2007, The Journal of Neuroscience.

[3]  Karl J. Friston,et al.  A free energy principle for the brain , 2006, Journal of Physiology-Paris.

[4]  R. Dolan,et al.  Distinct spatial frequency sensitivities for processing faces and emotional expressions , 2003, Nature Neuroscience.

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

[6]  P. Montoya,et al.  Low spatial frequency filtering modulates early brain processing of affective complex pictures , 2007, Neuropsychologia.

[7]  Y. Miyashita,et al.  Top-down signal from prefrontal cortex in executive control of memory retrieval , 1999, Nature.

[8]  Philippe G Schyns,et al.  Accurate statistical tests for smooth classification images. , 2005, Journal of vision.

[9]  Á. Pascual-Leone,et al.  Fast Backprojections from the Motion to the Primary Visual Area Necessary for Visual Awareness , 2001, Science.

[10]  Karl J. Friston Learning and inference in the brain , 2003, Neural Networks.

[11]  R. Knight,et al.  Prefrontal modulation of visual processing in humans , 2000, Nature Neuroscience.

[12]  Franco Lepore,et al.  Spatial Frequency Tuning during the Conscious and Non-Conscious Perception of Emotional Facial Expressions – An Intracranial ERP Study , 2012, Front. Psychology.

[13]  D. Ringach,et al.  Dynamics of Spatial Frequency Tuning in Macaque V1 , 2002, The Journal of Neuroscience.

[14]  Shlomo Bentin,et al.  Stimulus type, level of categorization, and spatial-frequencies utilization: implications for perceptual categorization hierarchies. , 2009, Journal of experimental psychology. Human perception and performance.

[15]  M. Tarr,et al.  Visual Object Recognition , 1996, ISTCS.

[16]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[17]  I Biederman,et al.  Do Background Depth Gradients Facilitate Object Identification? , 1981, Perception.

[18]  R. Watt Scanning from coarse to fine spatial scales in the human visual system after the onset of a stimulus. , 1987, Journal of the Optical Society of America. A, Optics and image science.

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

[20]  Martial Mermillod,et al.  Are Coarse Scales Sufficient for Fast Detection of Visual Threat? , 2010, Psychological science.

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

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

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

[24]  M. Bar,et al.  Cortical Analysis of Visual Context , 2003, Neuron.

[25]  M. Bar,et al.  The parahippocampal cortex mediates spatial and nonspatial associations. , 2007, Cerebral cortex.

[26]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[27]  P. O. Bishop,et al.  Spatial vision. , 1971, Annual review of psychology.

[28]  C. Gilbert,et al.  Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.

[29]  R. Zeelenberg,et al.  Emotion Improves and Impairs Early Vision , 2009, Psychological science.

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

[31]  Moshe Bar,et al.  Predictions: a universal principle in the operation of the human brain , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[32]  J. Bullier Integrated model of visual processing , 2001, Brain Research Reviews.

[33]  Kenjiro Suzuki,et al.  Vortical structure and heat transfer enhancement in the wake behind a wing-type vortex generator in drag-reducing surfactant flow , 2002 .

[34]  D. Bub,et al.  Does face inversion change spatial frequency tuning? , 2010, Journal of experimental psychology. Human perception and performance.

[35]  M. Bar,et al.  Affective value and associative processing share a cortical substrate , 2013, Cognitive, affective & behavioral neuroscience.

[36]  T L Babb,et al.  Visual receptive fields and response properties of neurons in human temporal lobe and visual pathways. , 1983, Brain : a journal of neurology.

[37]  J. Faubert,et al.  Configural face encoding and spatial frequency information , 2003, Perception & psychophysics.

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

[39]  M. Tarr,et al.  Micro-Valences: Perceiving Affective Valence in Everyday Objects , 2012, Front. Psychology.

[40]  M. Bar,et al.  Top-down facilitation of visual object recognition: object-based and context-based contributions. , 2006, Progress in brain research.

[41]  Y. Hochberg A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[42]  A. Watson,et al.  A standard model for foveal detection of spatial contrast. , 2005, Journal of vision.

[43]  Gerald L. Clore,et al.  With Sadness Comes Accuracy; With Happiness, False Memory , 2005, Psychological science.

[44]  Antígona Martínez,et al.  Contributions of low and high spatial frequency processing to impaired object recognition circuitry in schizophrenia. , 2013, Cerebral cortex.

[45]  S. Ullman,et al.  Spatial Context in Recognition , 1996, Perception.

[46]  M. Bradley Natural selective attention: orienting and emotion. , 2009, Psychophysiology.

[47]  John H. R. Maunsell,et al.  Visual processing in monkey extrastriate cortex. , 1987, Annual review of neuroscience.

[48]  P. Lennie,et al.  Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. , 1984, The Journal of physiology.

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

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

[51]  A. Borst Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[52]  R. Ratcliff Methods for dealing with reaction time outliers. , 1993, Psychological bulletin.

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

[54]  S Ullman,et al.  Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. , 1995, Cerebral cortex.

[55]  Moshe Bar,et al.  See it with feeling: affective predictions during object perception , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[56]  S. Grossberg Biological competition: Decision rules, pattern formation, and oscillations. , 1980, Proceedings of the National Academy of Sciences of the United States of America.

[57]  Nathalie Guyader,et al.  The coarse-to-fine hypothesis revisited: Evidence from neuro-computational modeling , 2005, Brain and Cognition.

[58]  N. Guyader,et al.  Is Coarse-to-Fine Strategy Sensitive to Normal Aging? , 2012, PloS one.

[59]  Tad T. Brunyé,et al.  Happiness by association: Breadth of free association influences affective states , 2013, Cognition.

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