The neural architecture of language: Integrative modeling converges on predictive processing
Name
e2105646118.full.pdf
Description
Published version
Size
1.33 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Schrimpf, Martin
Blank, Idan Asher
Tuckute, Greta
Kauf, Carina
Hosseini, Eghbal A
Kanwisher, Nancy
Tenenbaum, Joshua B
Fedorenko, Evelina
Date Issued
November 9, 2021
Journal
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Citation
Schrimpf, Martin, Blank, Idan Asher, Tuckute, Greta, Kauf, Carina, Hosseini, Eghbal A et al. 2021. "The neural architecture of language: Integrative modeling converges on predictive processing." Proceedings of the National Academy of Sciences, 118 (45).
Version
Final published version
Abstract
The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a systematic study taking this approach to higher-level cognition: human language processing, our species’ signature cognitive skill. We find that the most powerful “transformer” models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (functional MRI and electrocorticography). Models’ neural fits (“brain score”) and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Center for Brains, Minds, and Machines
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.2105646118
