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Driving and suppressing the human language network using large language models
Transformer models such as GPT generate human-like language and are highly predictive of human brain responses to language. Here, using fMRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of brain response associated with...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120732/ https://www.ncbi.nlm.nih.gov/pubmed/37090673 http://dx.doi.org/10.1101/2023.04.16.537080 |
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author | Tuckute, Greta Sathe, Aalok Srikant, Shashank Taliaferro, Maya Wang, Mingye Schrimpf, Martin Kay, Kendrick Fedorenko, Evelina |
author_facet | Tuckute, Greta Sathe, Aalok Srikant, Shashank Taliaferro, Maya Wang, Mingye Schrimpf, Martin Kay, Kendrick Fedorenko, Evelina |
author_sort | Tuckute, Greta |
collection | PubMed |
description | Transformer models such as GPT generate human-like language and are highly predictive of human brain responses to language. Here, using fMRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of brain response associated with each sentence. Then, we 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 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 noninvasively control neural activity in higher-level cortical areas, like the language network. |
format | Online Article Text |
id | pubmed-10120732 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-101207322023-04-22 Driving and suppressing the human language network using large language models Tuckute, Greta Sathe, Aalok Srikant, Shashank Taliaferro, Maya Wang, Mingye Schrimpf, Martin Kay, Kendrick Fedorenko, Evelina bioRxiv Article Transformer models such as GPT generate human-like language and are highly predictive of human brain responses to language. Here, using fMRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of brain response associated with each sentence. Then, we 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 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 noninvasively control neural activity in higher-level cortical areas, like the language network. Cold Spring Harbor Laboratory 2023-10-30 /pmc/articles/PMC10120732/ /pubmed/37090673 http://dx.doi.org/10.1101/2023.04.16.537080 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Tuckute, Greta Sathe, Aalok Srikant, Shashank Taliaferro, Maya Wang, Mingye Schrimpf, Martin Kay, Kendrick Fedorenko, Evelina Driving and suppressing the human language network using large language models |
title | Driving and suppressing the human language network using large language models |
title_full | Driving and suppressing the human language network using large language models |
title_fullStr | Driving and suppressing the human language network using large language models |
title_full_unstemmed | Driving and suppressing the human language network using large language models |
title_short | Driving and suppressing the human language network using large language models |
title_sort | driving and suppressing the human language network using large language models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120732/ https://www.ncbi.nlm.nih.gov/pubmed/37090673 http://dx.doi.org/10.1101/2023.04.16.537080 |
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