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fMRI reveals language-specific predictive coding during naturalistic sentence comprehension
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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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“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 (”
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Menashe, B. A., Drake, A., & Ben‐Shachar, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-62526-y
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Metadata
- 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
- Venue
- Neuropsychologia
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 138
- Pages
- 107307
- ISSN
- 0028-3932 (ISSN-L)
- 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
- FWCI
- 11.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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