Language learning shapes visual category-selectivity in deep neural networks

Zitong Lu; Yuxin Wang

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arXiv · 2502.16456

Language learning shapes visual category-selectivity in deep neural networks

Zitong Lu·Yuxin Wang

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Abstract

Category-selective regions in the human brain-such as the fusiform face area (FFA), extrastriate body area (EBA), parahippocampal place area (PPA), and visual word form area (VWFA)-support high-level visual recognition. Here, we investigate whether artificial neural networks (ANNs) exhibit analogous category-selective neurons and how these representations are shaped by language experience. Using an fMRI-inspired functional localizer approach, we identified face-, body-, place-, and word-selective neurons in deep networks presented with category images and scrambled controls. Both the purely visual ResNet and a linguistically supervised Lang-Learned ResNet contained category-selective neurons that increased in proportion across layers. However, compared to the vision-only model, the Lang-Learned ResNet showed a greater number but lower specificity of category-selective neurons, along with reduced spatial localization and attenuated activation strength-indicating a shift toward more distributed, semantically aligned coding. These effects were replicated in the large-scale vision-language model CLIP. Together, our findings reveal that language experience systematically reorganizes visual category representations in ANNs, providing a computational parallel to how linguistic context may shape categorical organization in the human brain.

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Zitong Lu, Yuxin Wang. 2025-10-09. Language learning shapes visual category-selectivity in deep neural networks. https://arxiv.org/abs/2502.16456

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