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

Prior studies show that visual imagery is a top-down reinstatement of visual perception, which likely extends to domains of visual expertise. But while self-reported vividness of mental images has been found to predict activity levels in visual areas, we found that self-reported vividness of mental images of cars is unrelated to subjects’ perceptual expertise with cars (Sunday et al., 2016). To further investigate this, we use functional MRI and multi-voxel pattern analysis to ask if car experts engage the fusiform face area during mental imagery of cars.

We repeatedly find that car recognition is independent from the recognition of other object categories, so the category seems to be special. We want to see if this independence is modulated by experience perceiving cars (similar to what has been found for face recognition in Balas & Saville, 2015). To test this, we will determine if cars are less independent from other object categories in a population living in a small town than a larger town. Since people from small towns likely have less experience perceiving cars, we predict that their car recognition ability will be less “specialized” than for people from larger cities. We are collaborating with researchers at the University of Nebraska-Lincoln to conduct experiments testing this hypothesis.

This work is supported by a NSF fellowship to Mackenzie Sunday.
Collaboration with Mike Dodd at University of Nebraska.