This research project will study how brain systems for face perception differ across individuals. Face perception plays a central role in interactions among people. The ability to recognize faces and interpret expressions can vary greatly in healthy adults. Efficient face perception develops slowly through childhood and into early adulthood. Face perception is much more efficient for familiar individuals with whom we interact frequently. We will use new, state-of-the-art methods for modeling the brain's system for face perception. The model has interacting processing pathways. Each pathway serves a different function. These include recognition of identity, interpretation of expression, and activation of social knowledge. We study individual differences using a new approach, called hyperalignment. Hyperalignment allows us to see how information is encoded in fine-scale brain patterns. These studies can make it possible to address questions about the effects of development, education, culture, and clinical disorder on brain organization.
The project will investigate individual variation in the human cortical functional architecture for face perception that leverages our previous work on multivariate models of information in the distributed neural system for face perception and our work creating hyperalignment to build high-dimensional common models of information spaces in cortex. Our approach discovers shared basis functions for information that is encoded in fine-scale cortical topographies, affording reliable measurement of individual differences in the representation of this detailed information. We will investigate individual variation in cortical systems for face perception as a function of cognitive ability, development, and learning, and build the common model of cortical information spaces using fMRI data collected during viewing of naturalistic movies and in the resting state. We will use response hyperalignment and connectivity hyperalignment to derive a common model of the face perception system with shared basis functions for fine-scale variation in response tuning and functional connectivity. By modeling shared neural representation at a fine scale, measures of deviations from shared representation are more sensitive to the inter-individual variation that underlies differences in cognitive function. Our methods have the potential to provide a firmer and more nuanced basis for addressing questions about the effects of development, education, culture, and clinical disorder on brain organization.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
