Driving and suppressing the human language network using large language models

Greta Tuckute (), Aalok Sathe, Shashank Srikant, Maya Taliaferro, Mingye Wang, Martin Schrimpf, Kendrick Kay and Evelina Fedorenko ()
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Greta Tuckute: Massachusetts Institute of Technology
Aalok Sathe: Massachusetts Institute of Technology
Shashank Srikant: Massachusetts Institute of Technology
Maya Taliaferro: Massachusetts Institute of Technology
Mingye Wang: Massachusetts Institute of Technology
Martin Schrimpf: Massachusetts Institute of Technology
Kendrick Kay: University of Minnesota
Evelina Fedorenko: Massachusetts Institute of Technology

Nature Human Behaviour, 2024, vol. 8, issue 3, 544-561

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41562-023-01783-7

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