Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines Article Swipe
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
,
Se Catherine Kim
,
Colton Casto
,
Lora Fanda
,
Werner Doyle
,
Daniel Friedman
,
Patricia Dugan
,
Lucía Melloni
,
Roi Reichart
,
Sasha Devore
,
Adeen Flinker
,
Liat Hasenfratz
,
Omer Levy
,
Avinatan Hassidim
,
Michael P. Brenner
,
Yossi Matias
,
Kenneth A. Norman
,
Orrin Devinsky
,
Uri Hasson
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.12.02.403477
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.12.02.403477
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 are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 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 (i.e., prediction error signals); 3) both represent words as a function of the previous context. In support of these three principles, our findings indicate that: a) the neural activity before word-onset contains context-dependent predictive information about forthcoming words, even hundreds of milliseconds before the words are perceived; b) the neural activity after word-onset reflects the surprise level and prediction error; and c) autoregressive DLM contextual embeddings capture the neural representation of context-specific word meaning better than arbitrary or static semantic embeddings. Together, our findings suggest that autoregressive DLMs provide a novel and biologically feasible computational framework for studying the neural basis of language.
Related Topics
Concepts
Surprise
Computer science
Word (group theory)
Context (archaeology)
Artificial intelligence
Autoregressive model
Natural language processing
Representation (politics)
Natural language understanding
Language model
Natural language
Speech recognition
Machine learning
Psychology
Linguistics
Communication
Econometrics
Mathematics
Philosophy
Paleontology
Law
Politics
Biology
Political science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.12.02.403477
- https://www.biorxiv.org/content/biorxiv/early/2021/03/19/2020.12.02.403477.full.pdf
- OA Status
- green
- Cited By
- 52
- References
- 118
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3108672584
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3108672584Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.12.02.403477Digital Object Identifier
- Title
-
Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machinesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-03Full publication date if available
- Authors
-
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, Se Catherine Kim, Colton Casto, Lora Fanda, Werner Doyle, Daniel Friedman, Patricia Dugan, Lucía Melloni, Roi Reichart, Sasha Devore, Adeen Flinker, Liat Hasenfratz, Omer Levy, Avinatan Hassidim, Michael P. Brenner, Yossi Matias, Kenneth A. Norman, Orrin Devinsky, Uri HassonList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.12.02.403477Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/03/19/2020.12.02.403477.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/03/19/2020.12.02.403477.full.pdfDirect OA link when available
- Concepts
-
Surprise, Computer science, Word (group theory), Context (archaeology), Artificial intelligence, Autoregressive model, Natural language processing, Representation (politics), Natural language understanding, Language model, Natural language, Speech recognition, Machine learning, Psychology, Linguistics, Communication, Econometrics, Mathematics, Philosophy, Paleontology, Law, Politics, Biology, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
52Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3, 2023: 9, 2022: 15, 2021: 16Per-year citation counts (last 5 years)
- References (count)
-
118Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_year | 2020 |
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