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Combined evidence from artificial neural networks and human brain-lesion models reveals that language modulates vision in human perception

Nature Human Behaviour (2025)Cite this article

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Abstract

Comparing information structures in between deep neural networks (DNNs) and the human brain has become a key method for exploring their similarities and differences. Recent research has shown better alignment of vision–language DNN models, such as contrastive language–image pretraining (CLIP), with the activity of the human ventral occipitotemporal cortex (VOTC) than earlier vision models, supporting the idea that language modulates human visual perception. However, interpreting the results from such comparisons is inherently limited owing to the ‘black box’ nature of DNNs. Here we combine model–brain fitness analyses with human brain lesion data to examine how disrupting the communication pathway between the visual and language systems causally affects the ability of vision–language DNNs to explain the activity of the VOTC to address this. Across four diverse datasets, CLIP consistently captured unique variance in VOTC neural representations, relative to both label-supervised (ResNet) and unsupervised (MoCo) models. This advantage tended to be left-lateralized at the group level, aligning with the human language network. Analyses of 33 patients who experienced a stroke revealed that reduced white matter integrity between the VOTC and the language region in the left angular gyrus was correlated with decreased CLIP–brain correspondence and increased MoCo–brain correspondence, indicating a dynamic influence of language processing on the activity of the VOTC. These findings support the integration of language modulation in neurocognitive models of human vision, reinforcing concepts from vision–language DNN models. The sensitivity of model–brain similarity to specific brain lesions demonstrates that leveraging the manipulation of the human brain is a promising framework for evaluating and developing brain-like computer models.

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Fig. 1: Overview of the fMRI datasets, vision models and study 1 analysis schema.
Fig. 2: Intercorrelations among vision model RDMs and their alignment with human behaviour.
Fig. 3: Language effect in VOTC across datasets.
Fig. 4: Study 2 analysis workflow linking WM integrity and model–brain correspondence in patients with chronic stroke.
Fig. 5: WM integrity of left VOTC–left AG tract predicts model–brain correspondence of CLIP and MoCo (n = 33 patients).
Fig. 6: Validation analyses using vision models trained on the identical dataset.

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Data availability

The data that support the findings of this study are available via figshare at https://doi.org/10.6084/m9.figshare.29531288.v3 (ref. 70). The original neuroimaging data are not publicly available owing to ethical constraints. De-identified data may be accessed by researchers who meet the criteria upon reasonable request: study 1 via the corresponding author (ybi@pku.edu.cn); study 2 via the Ethics Committee of the First Hospital of Shanxi Medical University (phone: +86 351 4639242) or the corresponding author (ybi@pku.edu.cn). Eligible requests will receive a response within 2 weeks.

Code availability

The custom codes that support the findings of this study are available via figshare at https://doi.org/10.6084/m9.figshare.29531288.v3 (ref. 70).

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Acknowledgements

This research was supported by grants from the STI2030-Major Project 2021ZD0204100 (grant no. 2021ZD0204104 to Y.B.); the National Natural Science Foundation of China (grant nos. 31925020 and 82021004 to Y.B.; grant no. 62376009 to Y.Z.; grant no. 32171052 to Xiaosha Wang; 62406020 to W.H.); the Fundamental Research Funds for the Central Universities (Y.B.); and the PKU-BingJi Joint Laboratory for Artificial Intelligence (Y.Z.). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank H. Yang, Z. Xiong, Z. Fu and H. Wen for their valuable comments on earlier drafts of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Haoyang Chen, Bo Liu.

  2. These authors jointly supervised this work: Xiaochun Wang, Yixin Zhu, Yanchao Bi.

Authors and Affiliations

  1. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

    Haoyang Chen, Yixin Zhu & Yanchao Bi

  2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Haoyang Chen, Shuyue Wang, Xiaosha Wang & Yanchao Bi

  3. Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China

    Bo Liu & Xiaochun Wang

  4. College of Medical Imaging, Shanxi Medical University, Taiyuan, China

    Bo Liu & Xiaochun Wang

  5. Shanxi Key Laboratory of Intelligent Imaging, First Hospital of Shanxi Medical University, Taiyuan, China

    Bo Liu & Xiaochun Wang

  6. School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China

    Wenjuan Han

  7. Institute for Artificial Intelligence, Peking University, Beijing, China

    Yixin Zhu & Yanchao Bi

  8. State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China

    Yixin Zhu

  9. Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China

    Yixin Zhu & Yanchao Bi

  10. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

    Yanchao Bi

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Contributions

Y.B., Y.Z. and W.H. conceived the study. H.C., B.L., Xiaosha Wang and Xiaochun Wang designed the experiment. H.C., B.L. and S.W. implemented and conducted the experiments. H.C. and B.L. analysed the data. H.C. and Y.B. wrote the initial draft. All authors reviewed and edited the Article.

Corresponding authors

Correspondence to Xiaochun Wang, Yixin Zhu or Yanchao Bi.

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Nature Human Behaviour thanks Guadalupe Dávila, Francesca Setti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 White matter integrity of the bilateral VOTC–LeftAG tract predicts model–brain correspondence of CLIP and MoCo (n = 33 patients).

Correlation coefficients and one-tailed P values are displayed on the plots (d.f.=30). a. Partial correlations between bilateral VOTC–LeftAG tract integrity and model–brain correspondence, controlling for lesion volume. Both sentence description effects (CLIP-specific) and MoCo-specific effects correlate significantly with VOTC–LeftAG tract integrity. b. Validation analysis using connections with right AG shows no significant relationships, confirming left-lateralized pathway specificity.

Extended Data Fig. 2 Low-level versus higher-level visual feature dependencies in WM–neural representation relationships (n = 33 patients).

Pearson correlations (d.f.=30) between left (a) or bilateral (b) VOTC–AG tract FA values and model–brain correspondence, controlling for lesion volume. The left panel shows that GIST effects (low-level visual features) exhibit a negative trend with tract integrity, whereas the right panel demonstrates that MoCo-specific effects (controlling for CLIP, ResNet, and GIST) correlate significantly with WM integrity. Correlation coefficients and one-tailed P values are displayed on the plots.

Extended Data Fig. 3 Voxel-based FA–symptom mapping (VFSM) results for model–brain correspondence (n = 33 patients).

Whole-brain correlation analyses (Pearson’s correlations) examine the relationships between FA values of each voxel and Fisher z-transformed RSA correlations in VOTC across patients. Left column shows correlations with sentence description effects (CLIP-specific effect); middle column displays MoCo-specific effects; right column indicates voxels with significant correlations for both conditions. Colour bars represent t-statistics from correlation analyses controlling for lesion volume. Results are thresholded at voxel-level P < 0.005, one-tailed, and cluster-level FWE-corrected P < 0.05. Axial slices displayed in MNI coordinate space.

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Chen, H., Liu, B., Wang, S. et al. Combined evidence from artificial neural networks and human brain-lesion models reveals that language modulates vision in human perception. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02357-5

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