Functional Neuroimaging of Face and Object Representations in the Ventral Visual Pathway

Princeton University, Princeton NJ

Investigators

Abstract

Visual recognition of faces and other object categories evokes distinct, category-related patterns of response in the extrastriate cortices of the human ventral visual pathway. With funding from NSF, Dr. James V. Haxby is investigating the representations of within-category distinctions among faces in the ventral visual pathway of the human brain. Previously, he and his colleagues showed that the patterns of submaximal neural responses also carry category-related information, indicating that the representations of different object categories are overlapping, and that weak and strong neural responses are both integral to population responses that represent object appearance. However, previous studies only addressed the distinctions between representations for different object categories and only analyzed the consistency of patterns of response within but not across individual subjects. The objective of the funded research is to investigate the representation of distinctions between exemplars within a single category, namely human faces. Experiments are testing whether representations of within-category distinctions among faces have a more focal anatomical distribution in the ventral pathway, that is, whether within-category differences are represented in regions that respond maximally to faces or are more broadly distributed. Studies are (1) examining the distribution of neural responses to within-category stimulus changes, and (2) analyzing the patterns of response evoked by stimuli. Face appearance is manipulated along two computationally-defined dimensions, one that reflects continuous variation in gender-related appearance and a second that reflects continuous variation from lean to wide, to investigate the correlation between patterns of neural response and computationally-defined alterations of face appearance. With other investigators, in addition to using methods of topographic pattern analysis, more sophisticated methods (e.g. PCA, ICA) are being developed for dissecting patterns of response that can identify the parts or subspaces that carry different aspects of information. These studies represent a novel approach to elucidating the detailed structure of the representation of faces in the ventral visual pathway. The long-term goal is to discover the principles of organization that underlie the topography of face representations and, thereby, reveal the correspondence rules that relate patterns of neural response to computational descriptions of information about face appearance. These principles of organization for topographically-organized population responses also may be relevant for the study of other types of information, such as visual motion, audition, and language. Development of new methods for analyzing patterns of response in neuroimaging data will benefit the entire field of functional neuroimaging and will facilitate the development of a new perspective on the neural representation of abstract information. Using computational models to guide the research on neural representation of faces will advance understanding of how faces are recognized, how factors such as social stereotypes and race affect face recognition, and the role face perception plays in social communication. Broader impacts. This research involves the efforts of a postdoctoral fellow and graduate students. It is helping to build the research and education program in cognitive neuroscience at Princeton University. The research is being used in classes at Princeton and will serve as the basis for guiding student research projects and for helping researchers from other fields, such as social psychology, apply functional neuroimaging to related questions. Students from under-represented groups are encouraged to become involved with the research team and to complete research projects. Collaborations with computational modelers and applied mathematicians facilitate the learning of new computational methods by psychologists and serve to ignite interest in cognitive neuroscience in other disciplines.