Contrastive learning explains the emergence and function of visual category-selective regions.

ABSTRACT: Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework-contrastive coding-based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Further, lesions of these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles in information processing. Finally, these pre-identified units can predict neural responses in all corresponding face-, scene-, body-, and word-selective regions of hum

SUBMITTER: Prince JS 

PROVIDER: S-EPMC11423896 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature