The Fusiform Face Area responds automatically to statistical regularities optimal for face categorization

Authors

  • Roberto Caldara,

    Corresponding author
    1. Department of Psychology, University of Glasgow, United Kingdom
    2. Centre for Cognitive Neuroimaging, University of Glasgow, United Kingdom
    • Centre for Cognitive Neuroimaging, Department of Psychology, 58 Hillhead Street, Glasgow, G12 8QB, UK
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  • Mohamed L. Seghier

    1. Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College of London, United Kingdom
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Abstract

Statistical regularities pervade our perceptual world. Assuming that the human brain is tuned for satisfying the constraints of the visual environment, visual system computations should be optimized for processing such regularities. A socially relevant and highly recurrent homogenous pattern for which the brain has developed sensitivity is certainly the human face. Yet, for which statistical regularities the face sensitive regions are tuned for, and to what extent their detection occurs automatically is largely unexplored. Using fMRI we measured activations within the face sensitive areas for nonface symmetrical and asymmetrical curvilinear patterns with either more high-contrast elements in the upper or in the lower part. Faceness evaluation performed outside of the scanner showed that these patterns were not perceived as schematic faces. Noticeably, symmetry violations disrupted perception of faceness, despite objective image similarity measures showing high faceness values for those patterns. Among the faces sensitive regions, only the right Fusiform Face Area (FFA) showed sensitivity to symmetry. This region showed also greater responses to patterns with more elements in the upper part. Critically, the FFA's responses were more strongly correlated with the physical objective faceness properties of the stimuli than the perceived subjective faceness ratings of the observers. These findings provide direct evidence that the neural computations of the right FFA are tuned to curvilinear symmetrical patterns with high-contrasted elements in the upper part, which fit best with the physical structure of human faces. Such low-level geometrical regularities might be used by the FFA to automatically categorize visual shapes as faces. Hum Brain Mapp 2009. © 2008 Wiley-Liss, Inc.

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