Self-supervised learning yields representational signatures of category-selective cortex

The ventral visual stream contains category-selective regions with distinct feature tuning, most prominently the fusiform face area (FFA) and parahippocampal place area (PPA). Why do these brain regions exhibit distinct tuning properties? Recent work suggests that brain-like category-selective features emerge from a general visual learning mechanism without domain-specific biases. Here, we test this proposal by applying a functional localizer approach to both humans and self-supervised neural networks, identifying face- and scene-selective units in the brain and in models. We then compared fMRI and model responses across a broad stimulus set probing classic representational signatures of the FFA and PPA, including preferences relating to curvature, animacy, real-world size, mid-level features, face shapes, and spatial layout information. Category-selective model units largely recapitulate the distinct representational signatures of category-selective brain regions, capturing most of the effects in our test battery. Our findings demonstrate that domain-general learning objectives are sufficient to create humanlike category-selectivity, suggesting that the distinct representational signatures of category-selective cortex may emerge from a unified computational goal akin to self-supervised learning.