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

1. ABSTRACT 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. Competing Interest Statement The authors have declared no competing interest. Footnotes We completed the following revisions: Expanded the Discussion to more explicitly address the limitations of the current study and alternative domain-specialized models. Analyzed and discussed the ImageNet training distribution in relation to scene-selective model representations. Clarified the selection and interpretation of category-selective model units, including their generalization properties and the absence of stable selectivity in early layers. Revised figures and captions to improve clarity and interpretability