Rapid scene categorization: Role of spatial frequency order, accumulation mode and luminance contrast

Visual analysis follows a default, predominantly coarse-to-fine processing sequence. Low spatial frequencies (LSF) are processed more rapidly than high spatial frequencies (HSF), allowing an initial coarse parsing of visual input, prior to analysis of finer information. Our study investigated the influence of spatial frequency processing order, accumulation mode (i.e. how spatial frequency information is received as an input by the visual system, throughout processing), and differences in luminance contrast between spatial frequencies on rapid scene categorization. In Experiment 1, we used sequences composed of six filtered scenes, assembled from LSF to HSF (coarse-to-fine) or from HSF to LSF (fine-to-coarse) to test the effects of spatial frequency order. Spatial frequencies were either successive or additive within sequences to test the effects of spatial frequency accumulation mode. Results showed that participants categorized coarse-to-fine sequences more rapidly than fine-to-coarse sequences, irrespective of spatial frequency accumulation in the sequences. In Experiment 2, we investigated the extent to which differences in luminance contrast rather than in spatial frequency account for the advantage of coarse-to-fine over fine-to-coarse processing. Results showed that both spatial frequencies and luminance contrast account for a predominant coarse-to-fine processing, but that the coarse-to-fine advantage stems mainly from differences in spatial frequencies. Our study cautions against the use of contrast normalization in studies investigating spatial frequency processing. We argue that this type of experimental manipulation can impair the intrinsic properties of a visual stimulus. As the visual system relies on these to enable recognition, bias may be induced in strategies of visual analysis.

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

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

[3]  Michel Dojat,et al.  Retinotopic and Lateralized Processing of Spatial Frequencies in Human Visual Cortex during Scene Categorization , 2013, Journal of Cognitive Neuroscience.

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

[5]  Robert Sekuler,et al.  Learning to imitate novel motion sequences. , 2007, Journal of vision.

[6]  Tom Hartley,et al.  Selectivity for low-level features of objects in the human ventral stream , 2010, NeuroImage.

[7]  Maurizio Codispoti,et al.  Early Spatial Frequency Processing of Natural Images: An ERP Study , 2013, PloS one.

[8]  Erin M. Harley,et al.  Why is it easier to identify someone close than far away? , 2005, Psychonomic bulletin & review.

[9]  P. Bex,et al.  Spatial frequency, phase, and the contrast of natural images. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

[11]  Nathalie Guyader,et al.  Image phase or amplitude? Rapid scene categorization is an amplitude-based process. , 2004, Comptes rendus biologies.

[12]  C. Enroth-Cugell,et al.  Chapter 9 Visual adaptation and retinal gain controls , 1984 .

[13]  Yuji Takeda,et al.  Time course of the integration of spatial frequency-based information in natural scenes , 2010, Vision Research.

[14]  Sheng Li,et al.  The neural signature of spatial frequency-based information integration in scene perception , 2013, Experimental Brain Research.

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

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

[17]  Rainer Goebel,et al.  From Coarse to Fine? Spatial and Temporal Dynamics of Cortical Face Processing , 2010, Cerebral cortex.

[18]  Arthur P. Ginsburg,et al.  Spatial filtering and visual form perception. , 1986 .

[19]  N. Kanwisher,et al.  The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.

[20]  Vision Research , 1961, Nature.

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

[22]  Christoph M. Michel,et al.  Hemispheric specialization of human inferior temporal cortex during coarse-to-fine and fine-to-coarse analysis of natural visual scenes , 2005, NeuroImage.

[23]  C. Malsburg,et al.  The role of complex cells in object recognition , 2002, Vision Research.

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

[25]  J R Lishman,et al.  Temporal Integration of Spatially Filtered Visual Images , 1992, Perception.

[26]  H. Bourgeois,et al.  [Contrast sensitivity]. , 1987, L'Annee therapeutique et clinique en ophtalmologie.

[27]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[28]  Chantal Kemner,et al.  Is the early modulation of brain activity by fearful facial expressions primarily mediated by coarse low spatial frequency information? , 2009, Journal of vision.

[29]  Christoph M. Michel,et al.  The Neural Substrates and Timing of Top–Down Processes during Coarse-to-Fine Categorization of Visual Scenes: A Combined fMRI and ERP Study , 2010, Journal of Cognitive Neuroscience.

[30]  Maurizio Codispoti,et al.  Scene Identification and Emotional Response: Which Spatial Frequencies Are Critical? , 2011, The Journal of Neuroscience.

[31]  Antonio Schettino,et al.  Multiple synergistic effects of emotion and memory on proactive processes leading to scene recognition , 2013, NeuroImage.

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

[33]  Dennis M. Levi,et al.  Reaction time as a measure of suprathreshold grating detection , 1978, Vision Research.

[34]  Nathalie Guyader,et al.  Coarse-to-fine Categorization of Visual Scenes in Scene-selective Cortex , 2014, Journal of Cognitive Neuroscience.

[35]  Geoffrey M Boynton Contrast Gain in the Brain , 2005, Neuron.

[36]  H. Hughes,et al.  Global Precedence, Spatial Frequency Channels, and the Statistics of Natural Images , 1996, Journal of Cognitive Neuroscience.

[37]  Antonio Schettino,et al.  Valence-Specific Modulation in the Accumulation of Perceptual Evidence Prior to Visual Scene Recognition , 2012, PloS one.

[38]  Yuanzhen Li,et al.  Measuring visual clutter. , 2007, Journal of vision.

[39]  Poggio Gf Spatial properties of neurons in striate cortex of unanesthetized macaque monkey. , 1972 .

[40]  E. Halgren,et al.  Top-down facilitation of visual recognition. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[41]  Louise Kauffmann,et al.  The neural bases of spatial frequency processing during scene perception , 2014, Front. Integr. Neurosci..

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

[43]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[44]  Antonio Schettino,et al.  Brain dynamics of upstream perceptual processes leading to visual object recognition: A high density ERP topographic mapping study , 2011, NeuroImage.

[45]  D. Tolhurst,et al.  Amplitude spectra of natural images , 1992 .

[46]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

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

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

[49]  Meredith Wadman Privacy bill under fire from researchers , 1998, Nature.