Evidence map›Paper›PMID 42368745›Full record

ArticleImaging neuroscience (Cambridge, Mass.)

The language network responds robustly to sentences across tasks.

Ruimin Gao, Chandler Cheung, Matthew Siegelman, Alvincé L A Pongos, Hope H Kean, Alyx Tanner, Evelina Fedorenko, Anna A Ivanova

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. 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
  4. No evidence of theory of mind reasoning in the human language network.Cerebral cortex (New York, N.Y. : 1991) · 2023
    Article
  5. Article
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

8 authors.

Ruimin GaoSchool of Psychological and Brain Sciences, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0009-0007-0306-2433
Chandler CheungDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.
Matthew SiegelmanDepartment of Psychology, Columbia University, New York, NY, United States.
Alvincé L A PongosUniversity of California, Berkeley, CA, United States.
Hope H KeanDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States.
Alyx TannerDepartment of Psychology, New York University, New York, NY, United States.
Evelina FedorenkoDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States.
Anna A IvanovaSchool of Psychological and Brain Sciences, Georgia Institute of Technology, Atlanta, GA, United States.ORCID https://orcid.org/0000-0002-1184-8299

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A network of left frontal and temporal brain areas supports language comprehension and production, implementing computations related to word retrieval and combinatorial linguistic processing. Here, we ask: to what extent are responses to language in this language network stable across task contexts, and how does this stability compare to task sensitivity in the domain-general multiple demand (MD) network? Participants (n = 52) read sentences and nonword lists under six task conditions, including passive reading, reading with a memory probe after each stimulus, and reading and answering questions that require deep semantic engagement. The sentences > nonwords contrast isolated the same set of language-responsive voxels across all tasks; the locations of those voxels were participant-specific, highlighting the value of individual-specific functional localization. We, therefore, conclude that language localization is robust to task variation. We then examined the magnitudes and fine-grained activation patterns in these language-responsive voxels (the language network) and in the domain-general MD network, to test whether task demands modulate linguistic computations and/or recruit a distinct brain system. The language network responded robustly to sentences across all tasks, with somewhat higher responses to semantically engaging tasks. In contrast, the MD network responded to both sentences and nonwords in the presence of a task, which warrants caution when using language paradigms that include task demands, as such paradigms engage two independent networks. A multivariate analysis further revealed that stimulus information is more easily decodable in the language network, whereas task information is more decodable in the MD network. These results suggest that the language and MD networks perform complementary functions during task-driven language comprehension, with the language network primarily extracting information from linguistic input and the MD network determining the appropriate response to the task.

Indexed as

fMRIlanguage networkMultiple Demand networksentence comprehensiontask demands

Identifiers

PMID42368745
PMCPMC13308801

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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.