Evidence map›Paper›PMID 31874149›Full record

ArticleNeuropsychologia2020

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.

Cory Shain, Idan Asher Blank, Marten van Schijndel, William Schuler, Evelina Fedorenko

Registry-linked trialOpen access · greenAbstract read
In one paragraph

Article in Neuropsychologia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05222594 (Computational Neuroscience of Language Processing in the Human Brain), which is not on this map. Cited by 101 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
101citing papers in PubMed, 2 pooled it
11.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05222594 narecruitingnot on this mapstarted 2021, after this paper: background citation

Computational Neuroscience of Language Processing in the Human Brain

TypeinterventionalSponsorMassachusetts General HospitalRan2021 to 2027Enrolled40ConditionsLanguage, EpilepsyArmsBehavioral tasks during intracranial monitoring
3 · Its place in the literature

Who cites it

101 citing papers in PubMed, 2 syntheses or guidelines pooled it, 239 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Language Recovery after Brain Injury: A Structural Network Control Theory Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Trial
  4. Review
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  8. The Extended Language Network: Language-Responsive Brain Areas Whose Contributions to Language Remain To Be Discovered.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  9. Article
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  11. Article
  12. The Spatio-Temporal Dynamics of Phoneme Encoding in Aging and Aphasia.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  13. Article
  14. Article
  15. Article
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  17. Article
  18. The cerebellar components of the human language network.bioRxiv : the preprint server for biology · 2025
    Article
  19. Dynamic neuroplasticity of language networks: The intersection of bilingualism and epilepsy.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  20. Article

41 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Cory ShainThe Ohio State University, 43210, USA. Electronic address: shain.3@osu.edu.
Idan Asher BlankUniversity of California Los Angeles, 90024, USA; Massachusetts Institute of Technology, 02139, USA. Electronic address: iblank@psych.ucla.edu.
Marten van SchijndelCornell University, 14853, USA. Electronic address: mv443@cornell.edu.
William SchulerThe Ohio State University, 43210, USA; Massachusetts General Hospital, Program in Speech and Hearing Bioscience and Technology, 02115, USA. Electronic address: schuler@ling.osu.edu.
Evelina FedorenkoMassachusetts General Hospital, Program in Speech and Hearing Bioscience and Technology, 02115, USA. Electronic address: evelina9@mit.edu.
The Ohio State University · USCornell University · USMassachusetts General Hospital · USUniversity of California, Los Angeles · US

Funding

Functional reorganization of the language and domain-general multiple demand systems in aphasiaR01DC016950 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI FEDORENKO, EVELINA, KIRAN, SWATHI · 2019 to 2023
$3.2M
The neural architecture of pragmatic processingR01DC016607 · NIDCD · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI FEDORENKO, EVELINA · 2018 to 2022
$2.4M
fMRI investigations of the functional architecture of the language systemR00HD057522 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI FEDORENKO, EVELINA · 2014 to 2016
$738k
NICHD NIH HHS R00 HD057522NIDCD NIH HHS R01 DC016607NIDCD NIH HHS R01 DC016950
6 · The paper itself

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.

Indexed as

AdultBrainComprehensionExecutive FunctionFemaleHumansLanguageMagnetic Resonance ImagingMaleNerve NetPsycholinguisticsYoung AdultfMRILanguageMultiple demand networkNaturalisticPredictive codingSentence processingSurprisalSyntactic structure

Identifiers

PMID31874149
PMCPMC7140726
OpenAlexW2997938465

What OpenQuestion holds

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Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.