RT Journal Article SR Electronic T1 The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.06.26.174482 DO 10.1101/2020.06.26.174482 A1 Martin Schrimpf A1 Idan Blank A1 Greta Tuckute A1 Carina Kauf A1 Eghbal A. Hosseini A1 Nancy Kanwisher A1 Joshua Tenenbaum A1 Evelina Fedorenko YR 2020 UL http://biorxiv.org/content/early/2020/10/09/2020.06.26.174482.abstract AB 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 StatementThe authors have declared no competing interest.