Driving and suppressing the human language network using large language models.
Journal
Nature human behaviour
ISSN: 2397-3374
Titre abrégé: Nat Hum Behav
Pays: England
ID NLM: 101697750
Informations de publication
Date de publication:
03 Jan 2024
03 Jan 2024
Historique:
received:
06
05
2023
accepted:
10
11
2023
medline:
4
1
2024
pubmed:
4
1
2024
entrez:
3
1
2024
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 38172630
doi: 10.1038/s41562-023-01783-7
pii: 10.1038/s41562-023-01783-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : U.S. Department of Health & Human Services | NIH | NIH Blueprint for Neuroscience Research
ID : R01-DC016607
Organisme : U.S. Department of Health & Human Services | NIH | NIH Blueprint for Neuroscience Research
ID : R01-DC016950
Organisme : U.S. Department of Health & Human Services | NIH | NIH Blueprint for Neuroscience Research
ID : U01- NS121471
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
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