Individual differences in prefrontal coding of visual features

Abstract

Each of us perceives the world differently. What may underlie such individual differences in perception? Here, we characterize the lateral prefrontal cortexs role in vision using computational models, with a specific focus on individual differences. Using a 7T fMRI dataset, we found that encoding models relating visual features extracted from a deep neural network to brain responses to natural images robustly predict responses in patches of LPFC. We then explored the representational structures and screened for images with high predicted responses in LPFC. We observed more substantial individual differences in the coding schemes of LPFC compared to visual regions. Computational modeling suggests that the amplified individual differences could result from the random projection between sensory and high-level regions underlying flexible working memory. Our study demonstrates the under-appreciated role of LPFC in visual processing and suggests that LPFC may underlie the idiosyncrasies in how different individuals experience the visual world.