Shared computational principles for language processing in humans and deep language models

Ariel Goldstein, Zaid Zada, Eliav Buchnik, Mariano Schain, Amy Price, Bobbi Aubrey, Samuel A. Nastase, Amir Feder, Dotan Emanuel, Alon Cohen, Aren Jansen, Harshvardhan Gazula, Gina Choe, Aditi Rao, Catherine Kim, Colton Casto, Lora Fanda, Werner Doyle, Daniel Friedman, Patricia DuganLucia Melloni, Roi Reichart, Sasha Devore, Adeen Flinker, Liat Hasenfratz, Omer Levy, Avinatan Hassidim, Michael Brenner, Yossi Matias, Kenneth A. Norman, Orrin Devinsky, Uri Hasson

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.

Original languageEnglish
Pages (from-to)369-380
Number of pages12
JournalNature Neuroscience
Issue number3
StatePublished - Mar 2022
Externally publishedYes

Bibliographical note

Funding Information:
We thank A. Goldberg, R. Goldstein, S. Michelmann, M. Meshulam, M. Kumar, M. Slaney and A. Huth for technical and conceptual assistance that motivated and informed this paper’s writing. This work was supported by the National Institutes of Health under award numbers DP1HD091948 (to A.G., Z.Z., A.P., B.A., G.C., A.R., C.K., F.L., A.F. and U.H.), R01MH112566 (to S.A.N.) and R01NS109367-01 to A.F., Finding A Cure for Epilepsy and Seizures (FACES) and Schmidt Futures Foundation DataX Fund.

Publisher Copyright:
© 2022, The Author(s).


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