There is a fundamental gap in our understanding of the computational principles and neural mechanisms by which neural circuits represent complex objects like faces. This conceptual gap constitutes an important problem because, until it is filled, we will not be able to understand face recognition and the reasons for face blindness. The long-term goal is to understand the computational principles and neural mechanisms of face recognition and create a computer face-recognition system based on these principles. The overall objective of this proposal is the determination of the computational principles of local and global face feature coding in the brain's face-processing network. The central hypothesis is that high-level feature tuning in face-selective areas can be understood as the result of the statistical properties of stimulus space and general organizational features of the circuits that process these stimuli. The rationale for this proposal is that completion of the proposed research will provide an understanding, of how neural circuits generate a meaningful representation of complex visual shape, imposing critical constraints on theories of vision. The hypothesis will be tested by pursuing three specific aims, which will determine 1) neural mechanism for facial feature tuning, 2) neural mechanism for categorical face selectivity, and 3) neural mechanisms for transformations of feature tuning. The joint computational and experimental approach will integrate functional magnetic resonance imaging to localize face areas with electrophysiological recordings targeted to these regions and computational analyses of model systems. The approach is innovative through the tight coupling of theoretical principles and experimental validation and by developing novel theoretical and experimental methodologies. The proposed research is significant, because it will unravel principles of neural circuit function that are of general relevance for understanding visual object recognition and multi-node networks. Because the outcome is an advance in understanding circuit mechanisms of social perception, it will identify vulnerabilities of the face-processing system directly relevant to the understanding of face blindness, prosopagnosia, and of altered social perception in syndromes spanning autism spectrum disorders, fragile X, and Williams syndrome.

Public Health Relevance

The proposed research is relevant to public health because the neural mechanisms of face processing are essential to human social life, and altered social perception is characteristic of many pervasive neurodevelopmental disorders. Studies proposed in this application will identify the computational principles of face recognition circuitry and suggest how alterations to these circuits impair function. Thus the proposed research is relevant to NIH's mission to reduce illness and disability.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Flanders, Martha C
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Rockefeller University
Graduate Schools
New York
United States
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