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The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing

View ORCID ProfileMartin Schrimpf, View ORCID ProfileIdan Blank, Greta Tuckute, Carina Kauf, Eghbal A. Hosseini, Nancy Kanwisher, Joshua Tenenbaum, Evelina Fedorenko
doi: https://doi.org/10.1101/2020.06.26.174482
Martin Schrimpf
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
2McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
3Center for Brains, Minds and Machines, MIT, Cambridge, MA, USA
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  • ORCID record for Martin Schrimpf
  • For correspondence: msch@mit.edu evelina9@mit.edu
Idan Blank
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
4Psychology Department, UCLA, Los Angeles, CA, USA
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Greta Tuckute
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
5Media Lab, MIT, Cambridge, MA, USA
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Carina Kauf
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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Eghbal A. Hosseini
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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Nancy Kanwisher
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
2McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
3Center for Brains, Minds and Machines, MIT, Cambridge, MA, USA
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Joshua Tenenbaum
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
3Center for Brains, Minds and Machines, MIT, Cambridge, MA, USA
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Evelina Fedorenko
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
2McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
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  • For correspondence: msch@mit.edu evelina9@mit.edu
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Abstract

The neuroscience of perception has recently been revolutionized with an integrative reverse-engineering approach in which computation, brain function, and behavior are linked across many different datasets and many computational models. We here present a first systematic study taking this approach into higher-level cognition: human language processing, our species’ signature cognitive skill. We find that the most powerful ‘transformer’ networks predict neural responses at nearly 100% and generalize across different datasets and data types (fMRI, ECoG). Across models, significant correlations are observed among all three metrics of performance: neural fit, fit to behavioral responses, and accuracy on the next-word prediction task (but not other language tasks), consistent with the long-standing hypothesis that the brain’s language system is optimized for predictive processing. Model architectures with initial weights further perform surprisingly similar to final trained models, suggesting that inherent structure – and not just experience with language – crucially contributes to a model’s match to the brain.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* joint second/senior authors

  • ↵† joint second/senior authors

  • updated figure 1, intro, abstract, title to better reflect integrative benchmarking approach

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 09, 2020.
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The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing
Martin Schrimpf, Idan Blank, Greta Tuckute, Carina Kauf, Eghbal A. Hosseini, Nancy Kanwisher, Joshua Tenenbaum, Evelina Fedorenko
bioRxiv 2020.06.26.174482; doi: https://doi.org/10.1101/2020.06.26.174482
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The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing
Martin Schrimpf, Idan Blank, Greta Tuckute, Carina Kauf, Eghbal A. Hosseini, Nancy Kanwisher, Joshua Tenenbaum, Evelina Fedorenko
bioRxiv 2020.06.26.174482; doi: https://doi.org/10.1101/2020.06.26.174482

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