Encoding models uncover fine-grained feature selectivity for bodies, hands and tools

Abstract

Category-selective areas in the occipitotemporal cortex (OTC) are typically characterized by broad tuning, yet neuroimaging suggests a finer-grained organization reflecting distinct computational roles. We combined image-level fMRI with artificial neural network (ANN)-based encoding models to investigate the selectivity and feature sensitivity of category-selective areas in ventral and lateral OTC. Using densely sampled fMRI data in three participants across six sessions, we identified functional dissociations between body, hand, and tool responses at the individual image level. Area-specific encoding models accurately predicted responses to millions of novel images, maintaining clear category preferences. Importantly, comparisons between models trained on areas selective for the same category revealed distinct feature sensitivities consistent with the areas anatomical location and hemispheric lateralization. These findings provide evidence for fine-grained specialization within OTC and demonstrate how ANN-based encoding models can uncover the computational, feature-level basis of category selectivity.