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The neural architecture of language: Integrative modeling converges on predictive processing.
Schrimpf, Martin; Blank, Idan Asher; Tuckute, Greta; Kauf, Carina; Hosseini, Eghbal A; Kanwisher, Nancy; Tenenbaum, Joshua B; Fedorenko, Evelina.
Affiliation
  • Schrimpf M; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139; msch@mit.edu ngk@mit.edu evelina9@mit.edu.
  • Blank IA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Tuckute G; Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Kauf C; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Hosseini EA; Department of Psychology, University of California, Los Angeles, CA 90095.
  • Kanwisher N; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Tenenbaum JB; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Fedorenko E; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.
Proc Natl Acad Sci U S A ; 118(45)2021 11 09.
Article in En | MEDLINE | ID: mdl-34737231
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Neural Networks, Computer / Language / Models, Neurological Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Proc Natl Acad Sci U S A Year: 2021 Document type: Article Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Neural Networks, Computer / Language / Models, Neurological Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Proc Natl Acad Sci U S A Year: 2021 Document type: Article Country of publication: United States