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Driving and suppressing the human language network using large language models

Nature Human Behaviour volume 8, pages 544–561 (2024)Cite this article

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Abstract

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of the brain response associated with each sentence. We then use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress the activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also non-invasively control neural activity in higher-level cortical areas, such as the language network.

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Fig. 1: Overview of the procedure for encoding model development and stimulus selection for evaluation.
Fig. 2: Model-selected sentences successfully drive and suppress responses in the language network.
Fig. 3: The encoding model maintains high predictive performance for brain responses from three new participants to out-of-distribution sentences.
Fig. 4: LH language regions show a high degree of stimulus-related activity for linguistic input relative to other brain areas, and the LH language regions show functionally similar responses.
Fig. 5: Surprisal and several other sentence properties modulate responses in the language network.
Fig. 6: Experimental overview.

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Data availability

The data are publicly available and can be downloaded via the following repository: https://github.com/gretatuckute/drive_suppress_brains.

Code availability

The code is publicly available in the following repository: https://github.com/gretatuckute/drive_suppress_brains.

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Acknowledgements

This work was supported by the Amazon Fellowship from the Science Hub (administered by the MIT Schwarzman College of Computing) (G.T.); the International Doctoral Fellowship from the American Association of University Women (G.T.); the K. Lisa Yang ICoN Center Graduate Fellowship (G.T.); the MIT-IBM Watson AI Lab (S.S.); NIH award nos R01-DC016607 (E.F.), R01-DC016950 (E.F.) and U01-NS121471 (E.F.); and funds from the McGovern Institute for Brain Research (E.F.), the Simons Center for the Social Brain (E.F.) and the Brain and Cognitive Sciences Department (E.F.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank C. Casto, E. Lee and E. Gibson for their help on the project; C. Shain for comments on an earlier draft of the manuscript; and N. Kanwisher, N A. R. Murty, N. Zaslavsky, K. Kar, J. Prince, J. McDermott, B. Lipkin, A. Ivanova and U.-M. O’Reilly for valuable discussions. We would also like to acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT, and its support team (Steve Shannon and Atsushi Takahashi).

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Authors and Affiliations

  1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Greta Tuckute, Aalok Sathe, Maya Taliaferro, Mingye Wang & Evelina Fedorenko

  2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Greta Tuckute, Aalok Sathe, Maya Taliaferro, Mingye Wang, Martin Schrimpf & Evelina Fedorenko

  3. Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Shashank Srikant

  4. MIT-IBM Watson AI Lab, Cambridge, MA, USA

    Shashank Srikant

  5. Quest for Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA

    Martin Schrimpf

  6. Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Martin Schrimpf

  7. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA

    Kendrick Kay

  8. Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA

    Evelina Fedorenko

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  1. Greta Tuckute
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Conceptualization: G.T., M.S. and E.F. Methodology: G.T., M.S., K.K. and E.F. Software: G.T., A.S. and S.S. Validation: G.T., A.S., S.S. and M.W. Formal analysis: G.T., A.S., S.S. and M.W. Investigation (data collection): G.T., A.S. and M.T. Data curation: G.T. and M.T. Writing—original draft: G.T. and E.F. Writing—review and editing: G.T., A.S., S.S., M.T., M.W., M.S., K.K. and E.F. Visualization: G.T. Supervision: E.F. and K.K. Project administration: E.F. Funding acquisition: E.F.

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Correspondence to Greta Tuckute or Evelina Fedorenko.

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Nature Human Behaviour thanks Surampudi Bapiraju and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Tuckute, G., Sathe, A., Srikant, S. et al. Driving and suppressing the human language network using large language models. Nat Hum Behav 8, 544–561 (2024). https://doi.org/10.1038/s41562-023-01783-7

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