Keynotes | K&Ts | GACs | Talks | Posters | Search

Poster F in Poster Session F: Thursday, August 6, 1:45 – 3:30 pm, Kimmel Center, Shorin & Rosenthal Rooms

Different Features Shape Category Selectivity in Human Visual Cortex and Deep Neural Networks

Leonard E van Dyck¹, Katharina Dobs¹; ¹Justus Liebig Universität Gießen

Presenter: Leonard E van Dyck

Category-selective responses to faces, bodies, and scenes are well established in human visual cortex and computational models. However, most studies test only a limited number of categories, leaving it unclear whether selectivity is robust under broader sampling and whether similar features shape selectivity in cortex and models. Here, we measured selectivity for hundreds of categories in ventral temporal cortex (VTC) and deep neural networks (DNNs) and tested which behaviorally relevant dimensions best explain the degree of selectivity across categories. We found that under broad category sampling, selectivity remains robust but varies continuously. In VTC, the most selective categories aligned with classic domains (faces, bodies, scenes), and variability in selectivity was primarily explained by semantic dimensions. In DNNs, by contrast, the most selective categories were visually distinctive animals and objects, and variability in selectivity was explained by mixed visual-semantic dimensions. These findings suggest that selectivity remains robust across a broad category space but is largely shaped by different features in human visual cortex and current computational models.

Topic Area: Computational Models of Vision & Visual Cortex