Individuals vary in how well they recognize, remember, and categorize faces and objects. This collaborative network funded by NSF aims at a broad understanding of such individual differences across a wide range of visual abilities and domains, to elucidate how both variation in general visual abilities and specific visual experiences affect our visual behavior. The project combines (1) ultra-high-field brain imaging at 7Tesla to map functional and anatomical brain measures trajectories in visual learning, (2) modern psychometric methods to create the necessary measures of individual differences in high-level visual ability, and (3) “deep” convolutional neural network models of vision that will be informed by brain and behavioral measures. These state-of-the-art tools will be integrated to account for individual differences in brain and behavior and to predict visual learning and performance.
Isabel Gauthier, Vanderbilt
Tom Palmeri, Vanderbilt
Sun-Joo Cho, Vanderbilt
Mike Tarr, Carnegie Mellon
Deva Ramanan, Carnegie Mellon
Gary Cottrell, USCD