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Ariel Goldstein, Zaid Zada, Eliav Buchnik, Mariano Schain, Amy Rose Price, Bobbi Aubrey, Samuel A. Nastase, Amir Feder, Dotan Emanuel, Alon Cohen and 22 more
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.
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Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A. R., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., Jansen, A., Gazula, H., Choe, G., Rao, A., Kim, C., Casto, C., Fanda, L., Doyle, W., Friedman, D. J., … Hasson, U. (2022). Shared computational principles for language processing in humans and deep language models. Nature Neuroscience, 25(3), 369–380. https://doi.org/10.1038/s41593-022-01026-4
Goldstein, Ariel, et al. "Shared computational principles for language processing in humans and deep language models." Nature Neuroscience, vol. 25, no. 3, 2022, pp. 369–380, https://doi.org/10.1038/s41593-022-01026-4.
Goldstein, Ariel, Zaid Zada, Eliav Buchnik, Mariano Schain, Amy Rose Price, Bobbi Aubrey, Samuel A. Nastase, et al. "Shared computational principles for language processing in humans and deep language models." Nature Neuroscience 25, no. 3 (2022): 369–380. https://doi.org/10.1038/s41593-022-01026-4.
Goldstein A., Zada Z., Buchnik E. [и др.] Shared computational principles for language processing in humans and deep language models // Nature Neuroscience. — 2022. — Vol. 25, № 3. — P. 369–380. — URL: https://doi.org/10.1038/s41593-022-01026-4
@article{goldstein2022shared,
title = {{Shared computational principles for language processing in humans and deep language models}},
author = {Ariel Goldstein and Zaid Zada and Eliav Buchnik and Mariano Schain and Amy Rose Price and Bobbi Aubrey and Samuel A. Nastase and Amir Feder and Dotan Emanuel and Alon Cohen and Aren Jansen and Harshvardhan Gazula and Gina Choe and Aditi Rao and Catherine Kim and Colton Casto and Lora Fanda and Werner Doyle and Daniel J. Friedman and Patricia C. Dugan and Lucía Melloni and Roi Reichart and Sasha Devore and Adeen Flinker and Liat Hasenfratz and Omer Levy and Avinatan Hassidim and Michael P. Brenner and Yossi Matias and Kenneth A. Norman and Orrin Devinsky and Uri Hasson},
journal = {Nature Neuroscience},
year = {2022},
volume = {25},
number = {3},
pages = {369--380},
doi = {10.1038/s41593-022-01026-4},
url = {https://doi.org/10.1038/s41593-022-01026-4}
}TY - JOUR AU - Goldstein, Ariel AU - Zada, Zaid AU - Buchnik, Eliav AU - Schain, Mariano AU - Price, Amy Rose AU - Aubrey, Bobbi AU - Nastase, Samuel A. AU - Feder, Amir AU - Emanuel, Dotan AU - Cohen, Alon AU - Jansen, Aren AU - Gazula, Harshvardhan AU - Choe, Gina AU - Rao, Aditi AU - Kim, Catherine AU - Casto, Colton AU - Fanda, Lora AU - Doyle, Werner AU - Friedman, Daniel J. AU - Dugan, Patricia C. AU - Melloni, Lucía AU - Reichart, Roi AU - Devore, Sasha AU - Flinker, Adeen AU - Hasenfratz, Liat AU - Levy, Omer AU - Hassidim, Avinatan AU - Brenner, Michael P. AU - Matias, Yossi AU - Norman, Kenneth A. AU - Devinsky, Orrin AU - Hasson, Uri TI - Shared computational principles for language processing in humans and deep language models JO - Nature Neuroscience VL - 25 IS - 3 SP - 369 EP - 380 PY - 2022 DA - 2022/03/01 DO - 10.1038/s41593-022-01026-4 UR - https://doi.org/10.1038/s41593-022-01026-4 ER -
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