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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.

References

  1. Binder, J. R. et al. Human brain language areas identified by functional magnetic resonance imaging. J. Neurosci. 17, 353–362 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Fedorenko, E., Hsieh, P.-J., Nieto-Castañón, A., Whitfield-Gabrieli, S. & Kanwisher, N. New method for fMRI investigations of language: defining ROIs functionally in individual subjects. J. Neurophysiol. 104, 1177–1194 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Fedorenko, E. & Thompson-Schill, S. L. Reworking the language network. Trends Cogn. Sci. 18, 120–126 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lipkin, B. et al. Probabilistic atlas for the language network based on precision fMRI data from >800 individuals. Sci. Data 9, 529 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  5. MacSweeney, M. et al. Neural systems underlying British Sign Language and audio-visual English processing in native users. Brain J. Neurol. 125, 1583–1593 (2002).

    Article  Google Scholar 

  6. Deniz, F., Nunez-Elizalde, A. O., Huth, A. G. & Gallant, J. L. The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality. J. Neurosci. 39, 7722–7736 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hu, J. et al. Precision fMRI reveals that the language-selective network supports both phrase-structure building and lexical access during language production. Cereb. Cortex 33, 4384–4404 (2022).

  8. Malik-Moraleda, S. et al. An investigation across 45 languages and 12 language families reveals a universal language network. Nat. Neurosci. 25, 1014–1019 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fedorenko, E. & Blank, I. A. Broca’s area is not a natural kind. Trends Cogn. Sci. 24, 270–284 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bautista, A. & Wilson, S. M. Neural responses to grammatically and lexically degraded speech. Lang. Cogn. Neurosci. 31, 567–574 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fedorenko, E., Blank, I. A., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 203, 104348 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mesulam, M.-M. Primary progressive aphasia. Ann. Neurol. 49, 425–432 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Wilson, S. M. et al. Language mapping in aphasia. J. Speech Lang. Hear. Res. 62, 3937–3946 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving Language Understanding by Generative Pre-training Technical Report (OpenAI, 2018).

  15. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. NAACL-HLT 2019 (eds Burstein, J. et al.) 4171–4186 (Association for Computational Linguistics, 2019); https://doi.org/10.18653/v1/N19-1423

  16. Wilcox, E. G., Gauthier, J., Hu, J., Qian, P. & Levy, R. On the predictive power of neural language models for human real-time comprehension behavior. In Proc. 42nd Annual Meeting of the Cognitive Science Society (eds Denison, S. et al.) 1707–1713 (Cognitive Science Society, 2020).

  17. Shain, C., Meister, C., Pimentel, T., Cotterell, R. & Levy, R. P. Large-scale evidence for logarithmic effects of word predictability on reading time. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/4hyna (2022).

  18. Toneva, M. & Wehbe, L. Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). In Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (eds Wallach, H. et al.) 14954–14964 (Curran Associates, Inc., 2019).

  19. Gauthier, J. & Levy, R. Linking artificial and human neural representations of language. In Proc. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (eds Inui, K. et al.) 529–539 (Association for Computational Linguistics, 2019); https://doi.org/10.18653/v1/D19-1050

  20. Schrimpf, M. et al. The neural architecture of language: integrative modeling converges on predictive processing. Proc. Natl Acad. Sci. USA 118, e2105646118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Caucheteux, C. & King, J.-R. Brains and algorithms partially converge in natural language processing. Commun. Biol. 5, 134 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Goldstein, A. et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 25, 369–380 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Caucheteux, C., Gramfort, A. & King, J.-R. Evidence of a predictive coding hierarchy in the human brain listening to speech. Nat. Hum. Behav. 7, 430–441 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bashivan, P., Kar, K. & DiCarlo, J. J. Neural population control via deep image synthesis. Science 364, eaav9436 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Ponce, C. R. et al. Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell 177, 999–1009 (2019).

  26. Fedorenko, E., Behr, M. K. & Kanwisher, N. Functional specificity for high-level linguistic processing in the human brain. Proc. Natl Acad. Sci. USA 108, 16428–16433 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Blank, I., Kanwisher, N. & Fedorenko, E. A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations. J. Neurophysiol. 112, 1105–1118 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Paunov, A. M., Blank, I. A. & Fedorenko, E. Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension. J. Neurophysiol. 121, 1244–1265 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Blank, I. A. & Fedorenko, E. No evidence for differences among language regions in their temporal receptive windows. NeuroImage 219, 116925 (2020).

    Article  PubMed  Google Scholar 

  30. Prince, J. S. et al. Improving the accuracy of single-trial fMRI response estimates using GLMsingle. eLife 11, e77599 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Allen, E. J. et al. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat. Neurosci. 25, 116–126 (2022).

    Article  CAS  PubMed  Google Scholar 

  32. Duncan, J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn. Sci. 14, 172–179 (2010).

    Article  PubMed  Google Scholar 

  33. Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).

    Article  PubMed  Google Scholar 

  34. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lerner, Y., Honey, C. J., Silbert, L. J. & Hasson, U. Topographic mapping of a hierarchy of temporal receptive windows using a narrated story. J. Neurosci. 31, 2906–2915 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Honey, C. J., Thompson, C. R., Lerner, Y. & Hasson, U. Not lost in translation: neural responses shared across languages. J. Neurosci. 32, 15277–15283 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Blank, I. A. & Fedorenko, E. Domain-general brain regions do not track linguistic input as closely as language-selective regions. J. Neurosci. 37, 9999–10011 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Nieto-Castañón, A. & Fedorenko, E. Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses. NeuroImage 63, 1646–1669 (2012).

    Article  PubMed  Google Scholar 

  39. Braga, R. M., DiNicola, L. M., Becker, H. C. & Buckner, R. L. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J. Neurophysiol. 124, 1415–1448 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Demberg, V. & Keller, F. Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition 109, 193–210 (2008).

    Article  PubMed  Google Scholar 

  41. Smith, N. J. & Levy, R. The effect of word predictability on reading time is logarithmic. Cognition 128, 302–319 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Brothers, T. & Kuperberg, G. R. Word predictability effects are linear, not logarithmic: implications for probabilistic models of sentence comprehension. J. Mem. Lang. 116, 104174 (2021).

    Article  PubMed  Google Scholar 

  43. Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P. & van den Bosch, A. Prediction during natural language comprehension. Cereb. Cortex 26, 2506–2516 (2016).

    Article  PubMed  Google Scholar 

  44. Henderson, J. M., Choi, W., Lowder, M. W. & Ferreira, F. Language structure in the brain: a fixation-related fMRI study of syntactic surprisal in reading. NeuroImage 132, 293–300 (2016).

    Article  PubMed  Google Scholar 

  45. Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P. & de Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. Proc. Natl Acad. Sci. USA 119, e2201968119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Shain, C., Blank, I. A., van Schijndel, M., Schuler, W. & Fedorenko, E. fMRI reveals language-specific predictive coding during naturalistic sentence comprehension. Neuropsychologia 138, 107307 (2020).

    Article  PubMed  Google Scholar 

  47. Michaelov, J. A., Bardolph, M. D., Van Petten, C. K., Bergen, B. K. & Coulson, S. Strong prediction: language model surprisal explains multiple N400 effects. Neurobiol. Lang. https://doi.org/10.1162/nol_a_00105 (2023).

  48. Rayner, K. & Duffy, S. A. Lexical complexity and fixation times in reading: effects of word frequency, verb complexity, and lexical ambiguity. Mem. Cogn. 14, 191–201 (1986).

    Article  CAS  Google Scholar 

  49. Brysbaert, M., Warriner, A. B. & Kuperman, V. Concreteness ratings for 40 thousand generally known English word lemmas. Behav. Res. Methods 46, 904–911 (2014).

    Article  PubMed  Google Scholar 

  50. Arfé, B., Delatorre, P. & Mason, L. Effects of negative emotional valence on readers’ text processing and memory for text: an eye-tracking study. Read. Writ. 36, 1743–1768 (2022).

  51. Kuchinke, L. et al. Incidental effects of emotional valence in single word processing: an fMRI study. NeuroImage 28, 1022–1032 (2005).

    Article  PubMed  Google Scholar 

  52. Binder, J. R., Westbury, C. F., McKiernan, K. A., Possing, E. T. & Medler, D. A. Distinct brain systems for processing concrete and abstract concepts. J. Cogn. Neurosci. 17, 905–917 (2005).

    Article  CAS  PubMed  Google Scholar 

  53. Ferstl, E. C. & von Cramon, D. Y. Time, space and emotion: fMRI reveals content-specific activation during text comprehension. Neurosci. Lett. 427, 159–164 (2007).

    Article  CAS  PubMed  Google Scholar 

  54. Lau, J. H., Clark, A. & Lappin, S. Grammaticality, acceptability, and probability: a probabilistic view of linguistic knowledge. Cogn. Sci. 41, 1202–1241 (2017).

    Article  PubMed  Google Scholar 

  55. Hu, J., Gauthier, J., Qian, P., Wilcox, E. & Levy, R. P. A systematic assessment of syntactic generalization in neural language models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 1725–1744 (Association for Computational Linguistics, 2020).

  56. Kauf, C. et al. Event knowledge in large language models: the gap between the impossible and the unlikely. Cogn. Sci. 47, e13386 (2023).

  57. Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Anderson, A. J. et al. Multiple regions of a cortical network commonly encode the meaning of words in multiple grammatical positions of read sentences. Cereb. Cortex 29, 2396–2411 (2019).

    Article  PubMed  Google Scholar 

  59. Baron-Cohen, S., Wheelwright, S., Spong, A., Scahill, V. & Lawson, J. Are intuitive physics and intuitive psychology independent? A test with children with Asperger syndrome. J. Dev. Learn. Disord. 5, 47–78 (2001).

  60. Jack, A. I. et al. fMRI reveals reciprocal inhibition between social and physical cognitive domains. NeuroImage 66, 385–401 (2013).

    Article  PubMed  Google Scholar 

  61. Pallier, C. & Devauchelle, A.-D. Cortical representation of the constituent structure of sentences. Proc. Natl Acad. Sci. USA 108, 2522–2527 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Diachek, E., Blank, I., Siegelman, M., Affourtit, J. & Fedorenko, E. The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation. J. Neurosci. 40, 4536–4550 (2020).

  63. Wehbe, L. et al. Incremental language comprehension difficulty predicts activity in the language network but not the multiple demand network. Cereb. Cortex 31, 4006–4023 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Mellem, M. S., Jasmin, K. M., Peng, C. & Martin, A. Sentence processing in anterior superior temporal cortex shows a social-emotional bias. Neuropsychologia 89, 217–224 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Redcay, E., Velnoskey, K. R. & Rowe, M. L. Perceived communicative intent in gesture and language modulates the superior temporal sulcus. Hum. Brain Mapp. 37, 3444–3461 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Wehbe, L. et al. Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses. PLoS ONE 9, e112575 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Jain, S. & Huth, A. G. Incorporating context into language encoding models for fMRI. In Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (eds Bengio, S., et al.) 6628–6637 (Curran Associates, Inc., 2018).

  68. Toneva, M., Mitchell, T. M. & Wehbe, L. Combining computational controls with natural text reveals aspects of meaning composition. Nat. Comput. Sci. 2, 745–757 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Kozachkov, L., Kastanenka, K. V. & Krotov, D. Building transformers from neurons and astrocytes. Proc. Natl Acad. Sci. USA 120, e2219150120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Jang, J., Ye, S. & Seo, M. Can large language models truly understand prompts? A case study with negated prompts. In Proc. 1st Transfer Learning for Natural Language Processing Workshop (eds Albalak A. et al.) 52–62 (PMLR, 2023).

  71. Michaelov, J. A. & Bergen, B. K. Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers. In Findings of the Association for Computational Linguistics: ACL 2023 (eds Rogers, A. et al.) 14162–14174 (Association for Computational Linguistics, 2023).

  72. Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A. & Konkle, T. What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines? Preprint at bioRxiv https://doi.org/10.1101/2022.03.28.485868 (2023).

  73. DiCarlo, J. J., Zoccolan, D. & Rust, N. C. How does the brain solve visual object recognition? Neuron 73, 415–434 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Wang, X. & Bi, Y. Idiosyncratic Tower of Babel: individual differences in word-meaning representation increase as word abstractness increases. Psychol. Sci. 32, 1617–1635 (2021).

    Article  PubMed  Google Scholar 

  75. Cohen, L., Salondy, P., Pallier, C. & Dehaene, S. How does inattention affect written and spoken language processing? Cortex 138, 212–227 (2021).

    Article  PubMed  Google Scholar 

  76. Gratton, C. & Braga, R. M. Editorial overview: deep imaging of the individual brain: past, practice, and promise. Curr. Opin. Behav. Sci. 40, iii–vi (2021).

    Article  Google Scholar 

  77. Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106–154 (1962).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Tenney, I., Das, D. & Pavlick, E. BERT rediscovers the classical NLP pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (eds Korhonen, A. et al.) 4593–4601 (Association for Computational Linguistics, 2019).

  79. Li, B. Z., Nye, M. & Andreas, J. Implicit representations of meaning in neural language models. In Proc. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Vol. 1: Long Papers) (eds Zong, C. et al.) 1813–1827 (Association for Computational Linguistics, 2021); https://doi.org/10.18653/v1/2021.acl-long.143

  80. Unger, L. & Fisher, A. V. The emergence of richly organized semantic knowledge from simple statistics: a synthetic review. Dev. Rev. 60, 100949 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Keller, T. A., Carpenter, P. A. & Just, M. A. The neural bases of sentence comprehension: a fMRI examination of syntactic and lexical processing. Cereb. Cortex 11, 223–237 (2001).

    Article  CAS  PubMed  Google Scholar 

  82. Regev, T. I. et al. Neural populations in the language network differ in the size of their temporal receptive windows. Preprint at bioRxiv https://doi.org/10.1101/2022.12.30.522216 (2023).

  83. Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). In International Conference on Machine Learning (ICML 2018) (eds Dy, J. & Krause, A.) 2673–2682 (Proceedings of Machine Learning Research, 2018).

  84. Saxe, R. & Kanwisher, N. People thinking about thinking people: the role of the temporo-parietal junction in ‘theory of mind’. NeuroImage 19, 1835–1842 (2003).

    Article  CAS  PubMed  Google Scholar 

  85. Baldassano, C., Hasson, U. & Norman, K. A. Representation of real-world event schemas during narrative perception. J. Neurosci. 38, 9689–9699 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Deen, B. & Freiwald, W. A. Parallel systems for social and spatial reasoning within the cortical apex. Preprint at bioRxiv https://doi.org/10.1101/2021.09.23.461550 (2022).

  87. Jain, S., Vo, V. A., Wehbe, L. & Huth, A. G. Computational language modeling and the promise of in silico experimentation. Neurobiol. Lang. https://doi.org/10.1162/nol_a_00101 (2023).

  88. Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).

    Article  Google Scholar 

  89. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  90. Wolf, T. et al. Transformers: state-of-the-art natural language processing. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (eds Liu, Q. & Schlangen, D.) 38–45 (Association for Computational Linguistics, 2020); https://doi.org/10.18653/v1/2020.emnlp-demos.6

  91. Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113 (1971).

    Article  CAS  PubMed  Google Scholar 

  92. Nieto-Castanon, A. Handbook of Functional Connectivity Magnetic Resonance Imaging Methods in CONN (Hilbert, 2020); https://doi.org/10.56441/hilbertpress.2207.6598

  93. Ashburner, J. & Friston, K. J. Unified segmentation. NeuroImage 26, 839–851 (2005).

    Article  PubMed  Google Scholar 

  94. Rokem, A. & Kay, K. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. GigaScience 9, giaa133 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Vázquez-Rodríguez, B. et al. Gradients of structure–function tethering across neocortex. Proc. Natl Acad. Sci. USA 116, 21219–21227 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Mahowald, K. & Fedorenko, E. Reliable individual-level neural markers of high-level language processing: a necessary precursor for relating neural variability to behavioral and genetic variability. NeuroImage 139, 74–93 (2016).

    Article  PubMed  Google Scholar 

  97. Hale, J. A probabilistic Earley parser as a psycholinguistic model. In 2nd Meeting of the North American Chapter of the Association for Computational Linguistics (Association for Computational Linguistics, 2001).

  98. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  99. Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means. R package version 1.8.4-1 (2023).

  100. Friston, K., Ashburner, J., Kiebel, S., Nichols, T. & Penny, W. Statistical Parametric Mapping: The Analysis of Functional Brain Images (Elsevier, 2006).

  101. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9, 179–194 (1999).

    Article  CAS  PubMed  Google Scholar 

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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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