“Hyperalignment: modeling the shared information encoded in idiosyncratic fine-scale cortical topographies”
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Background: The Haxby Lab’s current research focuses on the development of computational methods for building models of representational spaces. They assume that distributed population responses encode information. Within a cortical field, a broad range of stimuli or cognitive states can be represented as different patterns of response. They use fMRI to measure these patterns of response and multivariate pattern (MVP) analysis to decode their meaning. They are currently developing methods that make it possible to decode an individual’s brain data using MVP classifiers that are based on other subjects’ data. They use a complex, natural stimulus to sample a broad range of brain representational states as a basis for building high-dimensional models of representational spaces within cortical fields. These models are based on response tuning functions that are common across subjects. Initially, they demonstrated the validity of such a model in ventral temporal cortex. They are working on building similar models in other visual areas and in auditory areas. They also plan to investigate representation of social cognition using this same conceptual framework.
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