The human visual system is organized as a parallel, hierarchical network, and successive stages of visual processing appear to represent increasingly complicated aspects of shape-related and semantic information. However, the way that shape-related and semantic information is represented across much of the visual hierarchy is still poorly understood. The primary goal of this proposal is to understand how information about object shape and semantic category is represented explicitly across mid- and high-level visual areas. To address this important issue we propose to undertake a series of human functional MRI (fMRI) studies, using both synthetic and natural movies. Data will be analyzed by means of a powerful voxel-wise modeling (VM) approach that has been developed in my laboratory over the past several years.
In Aim 1 we propose to measure human brain activity evoked by synthetic naturalistic movies, and to use VM to evaluate and compare several competing theories of shape representation across the entire visual cortex.
In Aim 2 we propose to use VM to evaluate and compare competing theories of semantic representation.
In Aim 3 we propose to use machine learning and and VM to discover new aspects of shape and semantic representation. These experiments will provide fundamental new insights about the representation of visual information across visual cortex.
Disorders of central vision can severely affect quality of life and the design of treatments and devices for improving visual function will depend critically on understanding the organization of visual cortex. We propose to use functional MRI and sophisticated computational data analysis and modeling procedures to evaluate and compare multiple theories of visual function. The results will reveal how visual information is represented across the several dozen distinct functional areas that constitute human visual cortex.
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