Academic Journal
Unifying the structures of language in a neural population code.
| Τίτλος: | Unifying the structures of language in a neural population code. |
|---|---|
| Συγγραφείς: | Nastase SA; Department of Psychology and Center for Computational Language Sciences, University of Southern California, Los Angeles, CA, USA. Electronic address: snastase@usc.edu., Zada Z; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA., Goldberg A; Department of Psychology, Princeton University, Princeton, NJ, USA., Hasson U; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA. |
| Πηγή: | Neuron [Neuron] 2026 Oct 07; Vol. 114 (19), pp. 3527-3549. Date of Electronic Publication: 2026 Aug 14. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Cell Press Country of Publication: United States NLM ID: 8809320 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-4199 (Electronic) Linking ISSN: 08966273 NLM ISO Abbreviation: Neuron Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Cambridge, Mass. : Cell Press, c1988- |
| Ιατρικοί όροι (MeSH): | Brain*/physiology , Language* , Models, Neurological*, Humans ; Large Language Models |
| Περίληψη: | Large language models (LLMs) have mastered human language in ways that no previous computational system has. While rule-based, symbolic systems sufficed for constrained, well-defined problems, they were not able to accommodate the context-sensitive expressivity of natural language. LLMs instead use statistical learning to encode the diversity of linguistic structures into a unified high-dimensional embedding space. Strikingly, this context-driven, distributed representation closely parallels neural population codes, suggesting that the human language system may have converged on a similar computational strategy. Drawing on a growing body of work at the intersection of artificial intelligence and cognitive neuroscience, we show that LLMs can serve as cognitively plausible models of the neural computations supporting language in the human brain. We conclude that explaining how language can emerge from neural population codes, in both biological and artificial systems, will not be achieved through the incremental refinement of algebraic-symbolic theories but will demand new theoretical paradigms. (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of interests The authors declare no competing interests. |
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| Grant Information: | R01 DC022534 United States DC NIDCD NIH HHS |
| Contributed Indexing: | Keywords: LLMs; connectionism; construction grammar; embedding space; large language models; neurolinguistics; representational geometry; statistical learning |
| Entry Date(s): | Date Created: 20260814 Date Completed: 20261007 Latest Revision: 20261009 |
| Update Code: | 20261009 |
| PubMed Central ID: | PMC13546754 |
| DOI: | 10.1016/j.neuron.2026.07.024 |
| PMID: | 42600614 |
| Βάση Δεδομένων: | MEDLINE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.doi.org/10.1016/j.neuron.2026.07.024? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
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| Header | DbId: cmedm DbLabel: MEDLINE An: 42600614 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unifying the structures of language in a neural population code. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Nastase+SA%22">Nastase SA</searchLink>; Department of Psychology and Center for Computational Language Sciences, University of Southern California, Los Angeles, CA, USA. Electronic address: snastase@usc.edu.<br /><searchLink fieldCode="AU" term="%22Zada+Z%22">Zada Z</searchLink>; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA.<br /><searchLink fieldCode="AU" term="%22Goldberg+A%22">Goldberg A</searchLink>; Department of Psychology, Princeton University, Princeton, NJ, USA.<br /><searchLink fieldCode="AU" term="%22Hasson+U%22">Hasson U</searchLink>; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%228809320%22">Neuron</searchLink> [Neuron] 2026 Oct 07; Vol. 114 (19), pp. 3527-3549. <i>Date of Electronic Publication: </i>2026 Aug 14. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Review – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Cell+Press%22">Cell Press </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>8809320 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1097-4199 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2208966273%22">08966273 </searchLink><i>NLM ISO Abbreviation: </i>Neuron <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: [Cambridge, Mass. : Cell Press, c1988- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Language%22">Language*</searchLink> <br /><searchLink fieldCode="MM" term="%22Models%2C+Neurological%22">Models, Neurological*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Large+Language+Models%22">Large Language Models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Large language models (LLMs) have mastered human language in ways that no previous computational system has. While rule-based, symbolic systems sufficed for constrained, well-defined problems, they were not able to accommodate the context-sensitive expressivity of natural language. LLMs instead use statistical learning to encode the diversity of linguistic structures into a unified high-dimensional embedding space. Strikingly, this context-driven, distributed representation closely parallels neural population codes, suggesting that the human language system may have converged on a similar computational strategy. Drawing on a growing body of work at the intersection of artificial intelligence and cognitive neuroscience, we show that LLMs can serve as cognitively plausible models of the neural computations supporting language in the human brain. We conclude that explaining how language can emerge from neural population codes, in both biological and artificial systems, will not be achieved through the incremental refinement of algebraic-symbolic theories but will demand new theoretical paradigms.<br /> (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.) – Name: Abstract Label: Competing Interests Group: Ab Data: Declaration of interests The authors declare no competing interests. – Name: Ref Label: References Group: RefInfo Data: Trends Cogn Sci. 2012 Feb;16(2):114-21. (PMID: <searchLink fieldCode="PM" term="%2222221820%22">22221820)</searchLink><br />Proc Natl Acad Sci U S A. 2017 May 2;114(18):E3669-E3678. (PMID: <searchLink fieldCode="PM" term="%2228416691%22">28416691)</searchLink><br />PLoS One. 2014 Nov 26;9(11):e112575. (PMID: <searchLink fieldCode="PM" term="%2225426840%22">25426840)</searchLink><br />Neurobiol Lang (Camb). 2024 Apr 01;5(1):43-63. (PMID: <searchLink fieldCode="PM" term="%2238645622%22">38645622)</searchLink><br />Cognition. 1988 Mar;28(1-2):73-193. (PMID: <searchLink fieldCode="PM" term="%222450717%22">2450717)</searchLink><br />Front Syst Neurosci. 2008 Nov 24;2:4. (PMID: <searchLink fieldCode="PM" term="%2219104670%22">19104670)</searchLink><br />Trends Cogn Sci. 2019 Mar;23(3):235-250. (PMID: <searchLink fieldCode="PM" term="%2230704969%22">30704969)</searchLink><br />Nat Hum Behav. 2023 Sep;7(9):1526-1541. (PMID: <searchLink fieldCode="PM" term="%2237524930%22">37524930)</searchLink><br />Nat Hum Behav. 2025 Oct;9(10):2066-2078. (PMID: <searchLink fieldCode="PM" term="%2240500377%22">40500377)</searchLink><br />Nat Neurosci. 2016 Mar;19(3):356-65. (PMID: <searchLink fieldCode="PM" term="%2226906502%22">26906502)</searchLink><br />Nat Commun. 2024 Jun 29;15(1):5523. (PMID: <searchLink fieldCode="PM" term="%2238951520%22">38951520)</searchLink><br />Science. 2010 Nov 5;330(6005):845-51. 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(PMID: <searchLink fieldCode="PM" term="%2217071231%22">17071231)</searchLink> – Name: GrantInfo Label: Grant Information Group: Grant Data: R01 DC022534 United States DC NIDCD NIH HHS – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>LLMs; connectionism; construction grammar; embedding space; large language models; neurolinguistics; representational geometry; statistical learning – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260814 <i>Date Completed: </i>20261007 <i>Latest Revision: </i>20261009 – Name: DateUpdate Label: Update Code Group: Date Data: 20261009 – Name: PubmedCentralID Label: PubMed Central ID Group: ID Data: PMC13546754 – Name: DOI Label: DOI Group: ID Data: 10.1016/j.neuron.2026.07.024 – Name: AN Label: PMID Group: ID Data: 42600614 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.neuron.2026.07.024 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 3527 Subjects: – SubjectFull: Humans Type: general – SubjectFull: Large Language Models Type: general – SubjectFull: Brain physiology Type: general – SubjectFull: Language Type: general – SubjectFull: Models, Neurological Type: general Titles: – TitleFull: Unifying the structures of language in a neural population code. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nastase SA – PersonEntity: Name: NameFull: Zada Z – PersonEntity: Name: NameFull: Goldberg A – PersonEntity: Name: NameFull: Hasson U IsPartOfRelationships: – BibEntity: Dates: – D: 07 M: 10 Text: 2026 Oct 07 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1097-4199 Numbering: – Type: volume Value: 114 – Type: issue Value: 19 Titles: – TitleFull: Neuron Type: main |
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