van Dyck, L. E. | bioRxiv Science

bioRxiv2026

Face and body representations converge along the visual hierarchy in models and cortex

Human visual cortex contains regions specialized for faces and bodies, yet we perceive people as a whole. Why does the brain appear to segregate faces and bodies, and how are they integrated to support person perception? Here, we test whether deep neural network models optimized for visual recognition develop segregated or integrated face and body processing, and how this compares to fMRI activity in visual cortex during natural image viewing. We find that models contain face- and body-selective units but also mixed-selective units that are tuned to both faces and bodies. While face- and body-selective units explain unique variance in their corresponding cortical regions, mixed-selective units best explain activity across regions, and shared variance increases from posterior to anterior cortex. Together, our findings suggest that face and body processing reflects a balance of segregation and integration along the visual hierarchy in humans and models, supporting specialized yet flexible person perception.

neuroscience↗

bioRxiv2025

Multidimensional feature tuning in category-selective areas of human visual cortex

Human high-level visual cortex has been described in two seemingly opposed ways. A categorical view emphasizes discrete category-selective areas, while a dimensional view highlights continuous feature maps spanning across these areas. Can these divergent perspectives on cortical organization be reconciled within a unifying framework? Using data-driven decomposition of fMRI responses in face-, body-, and scene-selective areas, we identified overlapping activity patterns shared across individuals. Each area encoded multiple interpretable dimensions tuned to both finer subcategory features and coarser cross-category distinctions beyond its preferred category, even in the most category-selective voxels. These dimensions formed distinct clusters within category-selective areas but were also sparsely distributed across the broader visual cortex, supporting both locally selective, category-specific, and globally distributed, feature-based coding. Together, these findings suggest multidimensional tuning as a fundamental organizing principle that integrates feature-selective clusters, category-selective areas, and large-scale tuning maps, providing a more comprehensive understanding of category representations in human visual cortex.

neuroscience↗