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Cited by 215Neurobiology of Language and BilingualismReading and Literacy DevelopmentLanguage Development and Disorders

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

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Shain, Cory, Blank, Idan Asher, van Schijndel, Marten, Schuler, William, Fedorenko, Evelina·2020·Neuropsychologia
DOI 10.1016/j.neuropsychologia.2019.107307PMC PMC7140726

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Abstract

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.

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  • The human language network is sensitive to abstract hierarchical rules across isolated phrases, naturalistic narratives, and syntactically well-formed but semantically empty stimuli.

    “it is sensitive to abstract hierarchical rules in isolated phrases and sentences [31, 108, 109, 110], in naturalistic narratives [111, 112, 113, 114], and in syntactically well-formed but semantically empty (”

    From Dissociating language and thought in large language models (2024) · cites this paper as [111]

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  1. Broca P (1861). Remarks on the seat of the faculty of articulated language, following an observation of aphemia (loss of speech). Bulletin de La Société Anatomique, 6, 330– 357.
  2. Fedorenko E, Mineroff Z, Siegelman M, & Blank I (2018). Word meanings and sentence structure recruit the same set of fronto-temporal regions during comprehension. BioRxiv.
  3. Fodor J (1983). The modularity of mind: An essay on faculty psychology. Cambridge: MIT Press.
  4. Gibson E, & Ko K (1998). An integration-based theory of computational resources in sentence comprehension. In Fourth Architectures and Mechanisms in Language Processing Conference.
  5. Gloor P (1997). The temporal lobe \& limbic system. Oxford: Oxford University Press.
  6. Joshi AK (1985). How much context sensitivity is necessary for characterizing structural descriptions: Tree adjoining grammars In Dowty LKD & Zwicky A (Eds.), Natural language parsing: Psychological, computational and theoretical perspectives (pp. 206– 250). Cambridge, U.K.: Cambridge University Press.
  7. Mollica F, Siegelman M, Diachek E, Piantadosi ST, Mineroff Z, Futrell R, & Fedorenko E (2018). High local mutual information drives the response in the human language network. BioRxiv, 436204.
  8. Shain C (2019). Prediction is all you need: A large-scale study of the effects of word frequency and predictability in naturalistic reading. Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Authors
Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E.
Affiliations
  • The Ohio State University (US)
  • University of California, Los Angeles (US)
  • Massachusetts General Hospital (US)
Corresponding author
Shain, Cory
ORCID
Venue
Neuropsychologia
Publisher
Elsevier BV
Published
2020-02
Volume
138
Pages
107307
ISSN
0028-3932 (ISSN-L)
DOI
10.1016/j.neuropsychologia.2019.107307
PMC
PMC7140726
OpenAlex
W2997938465
Cited by
215
References
325
Citations by year
2026: 18 · 2025: 42 · 2024: 34 · 2023: 43 · 2022: 28 · 2021: 31 · 2020: 15 · 2019: 3 · 2018: 1
FWCI
11.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuropsychologia (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Simons Foundation
  • National Institutes of Health (R01-DC-016607, R01-DC-016950, R00-HD-057522)
  • Massachusetts Institute of Technology
  • National Science Foundation (R01-DC-016950, R01-DC-016607, 1816891)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
  • Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
  • Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
Keywords
predictive coding, sentence comprehension, word predictability, word co-occurrence, syntactic hierarchy, language network, multiple-demand network, executive function, hemodynamic response function, BOLD signal fluctuations
MeSH terms
Adult, Brain, Female, Humans, Language, Magnetic Resonance Imaging, Male, Nerve Net, Psycholinguistics, Comprehension, Young Adult, Executive Function

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