The Neural Bases of the Semantic Interference of Spatial Frequency-based Information in Scenes

Current models of visual perception suggest that during scene categorization, low spatial frequencies (LSF) are processed rapidly and activate plausible interpretations of visual input. This coarse analysis would then be used to guide subsequent processing of high spatial frequencies (HSF). The present fMRI study examined how processing of LSF may influence that of HSF by investigating the neural bases of the semantic interference effect. We used hybrid scenes as stimuli by combining LSF and HSF from two different scenes, and participants had to categorize the HSF scene. Categorization was impaired when LSF and HSF scenes were semantically dissimilar, suggesting that the LSF scene was processed automatically and interfered with categorization of the HSF scene. fMRI results revealed that this semantic interference effect was associated with increased activation in the inferior frontal gyrus, the superior parietal lobules, and the fusiform and parahippocampal gyri. Furthermore, a connectivity analysis (psychophysiological interaction) revealed that the semantic interference effect resulted in increasing connectivity between the right fusiform and the right inferior frontal gyri. Results support influential models suggesting that, during scene categorization, LSF information is processed rapidly in the pFC and activates plausible interpretations of the scene category. These coarse predictions would then initiate top–down influences on recognition-related areas of the inferotemporal cortex, and these could interfere with the categorization of HSF information in case of semantic dissimilarity to LSF.

[1]  R. Poldrack,et al.  Dissociable Controlled Retrieval and Generalized Selection Mechanisms in Ventrolateral Prefrontal Cortex , 2005, Neuron.

[2]  E. DeYoe,et al.  Concurrent processing in the primate visual cortex. , 1995 .

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

[4]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[5]  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.

[6]  P Girard,et al.  Feedback connections act on the early part of the responses in monkey visual cortex. , 2001, Journal of neurophysiology.

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

[8]  Leslie G. Ungerleider,et al.  Distributed representation of objects in the human ventral visual pathway. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[9]  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.

[10]  H Garavan,et al.  A midline dissociation between error-processing and response-conflict monitoring , 2003, NeuroImage.

[11]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[12]  Timothy E. J. Behrens,et al.  Tools of the trade: psychophysiological interactions and functional connectivity. , 2012, Social cognitive and affective neuroscience.

[13]  Karl J. Friston,et al.  Stochastic Designs in Event-Related fMRI , 1999, NeuroImage.

[14]  David J. Freedman,et al.  Categorical representation of visual stimuli in the primate prefrontal cortex. , 2001, Science.

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

[16]  S. Thorpe,et al.  Speed of processing in the human visual system , 1996, Nature.

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

[18]  S. Yantis,et al.  Transient neural activity in human parietal cortex during spatial attention shifts , 2002, Nature Neuroscience.

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

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

[21]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[22]  Maurizio Corbetta,et al.  Anatomical Segregation of Visual Selection Mechanisms in Human Parietal Cortex , 2013, The Journal of Neuroscience.

[23]  R. Henson,et al.  Frontal lobes and human memory: insights from functional neuroimaging. , 2001, Brain : a journal of neurology.

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

[25]  Nathalie Guyader,et al.  Rapid scene categorization: Role of spatial frequency order, accumulation mode and luminance contrast , 2015, Vision Research.

[26]  Sarah Shomstein,et al.  Cognitive functions of the posterior parietal cortex: top-down and bottom-up attentional control , 2012, Front. Integr. Neurosci..

[27]  I. THE ATTENTION SYSTEM OF THE HUMAN BRAIN , 2002 .

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

[29]  Russell A. Epstein,et al.  The Parahippocampal Place Area Recognition, Navigation, or Encoding? , 1999, Neuron.

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

[31]  Bradford Z. Mahon,et al.  A bimodal tuning curve for spatial frequency across left and right human orbital frontal cortex during object recognition. , 2014, Cerebral cortex.

[32]  D. Yves von Cramon,et al.  Variants of uncertainty in decision-making and their neural correlates , 2005, Brain Research Bulletin.

[33]  B McElree,et al.  Covert attention accelerates the rate of visual information processing , 2001, Proceedings of the National Academy of Sciences of the United States of America.

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

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

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

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

[38]  H. Garavan,et al.  Dissociable Executive Functions in the Dynamic Control of Behavior: Inhibition, Error Detection, and Correction , 2002, NeuroImage.

[39]  Adam Gazzaley,et al.  Neural Mechanisms Underlying the Impact of Visual Distraction on Retrieval of Long-Term Memory , 2010, The Journal of Neuroscience.

[40]  Stefan Pollmann,et al.  Evidence for feature binding in the superior parietal lobule , 2013, NeuroImage.

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

[42]  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.

[43]  K. Grill-Spector The neural basis of object perception , 2003, Current Opinion in Neurobiology.

[44]  Karl J. Friston,et al.  Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution , 2003, NeuroImage.

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

[46]  M. Bar The proactive brain: using analogies and associations to generate predictions , 2007, Trends in Cognitive Sciences.

[47]  M. Behrmann,et al.  Parietal cortex and attention , 2004, Current Opinion in Neurobiology.

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

[49]  Keiji Tanaka,et al.  Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.

[50]  Karl J. Friston,et al.  Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.

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

[52]  Monica Baciu,et al.  Cerebral regions and hemispheric specialization for processing spatial frequencies during natural scene recognition. An event-related fMRI study , 2004, NeuroImage.

[53]  J. Jonides,et al.  Interference resolution: Insights from a meta-analysis of neuroimaging tasks , 2007, Cognitive, affective & behavioral neuroscience.

[54]  Jared Abrams,et al.  Voluntary attention increases perceived spatial frequency , 2010, Attention, perception & psychophysics.

[55]  Jason B. Mattingley,et al.  Selective attention modulates inferior frontal gyrus activity during action observation , 2008, NeuroImage.

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

[57]  T. Hendler,et al.  A hierarchical axis of object processing stages in the human visual cortex. , 2001, Cerebral cortex.

[58]  Jonas Persson,et al.  Mapping interference resolution across task domains: A shared control process in left inferior frontal gyrus , 2009, Brain Research.

[59]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[60]  R. Poldrack,et al.  Recovering Meaning Left Prefrontal Cortex Guides Controlled Semantic Retrieval , 2001, Neuron.

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