There is a fundamental gap in our understanding of the neural mechanisms supporting the recognition of complexly structured information like faces. The existence of this conceptual gap constitutes an important problem because, until it is filled, we will not be able to explain face recognition and the reasons for its impairment in neurodevelopmental disorders like autism or face blindness. The long-term goal is to understand the neural mechanisms of face recognition and build an artificial face-recognition system implementing neural computations and thus explain face recognition mechanistically. The overall objective of this proposal is the identification of the neural mechanisms and computations by which face-processing systems support active face recognition. The experimental model system consists of interconnected brain areas, each with a unique functional specialization, currently thought to support face identification in a feed-forward manner. The central hypothesis is that the face-processing system is instead designed to make information at multiple levels of perceptual organization explicit from facial feature, to face, to person, to the layout and meaning of an entire scene. The hypothesis is rooted in a series of experimental observations in the principal applicant?s laboratory. The rationale for this proposal is that completion of the proposed research will provide an understanding, in a model system, of how neural populations generate compositional, structured representations, imposing critical constraints on theories of cortex, intelligence, and active vision. The hypothesis will be tested by pursuing three specific aims: 1) Determine Rules of Part-Whole Interaction for Face Recognition, 2) Determine Mechanisms of Context- Modulation in Face Areas, and 3) Determine Population Codes for Complex Visual Scene Processing. Functional magnetic resonance imaging to localize face and body areas will be combined with electrophysiological recordings targeted to these regions and analyses of computational model systems to determine how facial feature integration into the holistic structure of the face shapes face identification, to determine the mechanisms by which object and body context impact face representations, and to elucidate the extent and form of social information codes in these areas supporting active high-level scene processing. The approach is innovative, because it fundamentally challenges the standard model of the circuits of face-processing, aiming to reveal the mechanisms supporting information processing at multiple levels of perceptual organization with a focus on extracting general principles of cortical circuit operations for high-level vision. The proposed research is significant, because it will provide a mechanistic understanding of the operations of complex neural circuits and how they implement computations that extract and package socially highly important information. 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.
The proposed research is relevant to public health because the neural mechanisms that underlie face processing are essential to human social life, and altered social perception is characteristic of psychiatric disorders like autism spectrum disorders, face blindness, Fragile X, and Williams syndrome. Studies proposed in this application will examine how the brain processes visual information from features to objects and faces to their relationships, and will thus help uncover the neuronal mechanisms of face recognition and social perception and their impairments in neurodevelopmental disorders. Thus the proposed research is relevant to the part of the NIH?s mission that pertains to reducing illness and disability.
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