ArticleImaging neuroscience (Cambridge, Mass.)
The language network responds robustly to sentences across tasks.
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.
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Who cites it
5 citing papers in PubMed.
- The cerebellum specializes for language even in the absence of contralateral neocortical inputs.bioRxiv : the preprint server for biology · 2026Article
- Functional identification of language-responsive sensors in individual participants in MEG investigations.Journal of neurophysiology · 2026Article
- 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 · 2026Article
- No evidence of theory of mind reasoning in the human language network.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- A 3.5-minute-long reading-based fMRI localizer for the language network.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
8 authors.
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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.
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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.