Evidence map›Paper›PMID 38466812›Full record

ArticleCerebral cortex (New York, N.Y. : 1991)2024

Functional characterization of the language network of polyglots and hyperpolyglots with precision fMRI.

Saima Malik-Moraleda, Olessia Jouravlev, Maya Taliaferro, Zachary Mineroff, Theodore Cucu, Kyle Mahowald, Idan A Blank, Evelina Fedorenko

Abstract read
In one paragraph

Article in Cerebral cortex (New York, N.Y. : 1991), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

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.

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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. The emergence of the language system in the toddler brain.bioRxiv : the preprint server for biology · 2026
    Article
  7. Article
  8. The cerebellar components of the human language network.bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Constructed languages are processed by the same brain mechanisms as natural languages.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  11. Article
  12. Review
  13. High-level language brain regions process sublexical regularities.Cerebral cortex (New York, N.Y. : 1991) · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Saima Malik-MoraledaDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.ORCID 0000-0003-1224-5323
Olessia JouravlevDepartment of Cognitive Science, Carleton University, Ottawa K1S 5B6, Canada.
Maya TaliaferroDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.ORCID 0000-0002-0137-1546
Zachary MineroffEberly Center, Carnegie Mellon University, Pittsburgh, PA 15289, United States.ORCID 0000-0001-8806-9069
Theodore CucuDepartment of Psychology, Carnegie Mellon University, Pittsburgh, PA 15289, United States.ORCID 0000-0001-5468-3730
Kyle MahowaldDepartment of Linguistics, The University of Texas at Austin, Austin, TX 78712, United States.
Idan A BlankDepartment of Psychology, University of California Los Angeles, Los Angeles, CA 90095, United States.ORCID 0000-0001-7057-8391
Evelina FedorenkoDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.ORCID 0000-0003-3823-514X

Funding

Computational Neuroscience of Language Processing in the Human BrainU01NS121471 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI FEDORENKO, EVELINA, RICHARDSON, ROBERT MARK · 2021 to 2025
$3.2M
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
NIDCD NIH HHS R01 DC016607NIDCD NIH HHS R01 DC016950NIH HHS R01-DC016607NINDS NIH HHS U01 NS121471
6 · The paper itself

Abstract

How do polyglots-individuals who speak five or more languages-process their languages, and what can this population tell us about the language system? Using fMRI, we identified the language network in each of 34 polyglots (including 16 hyperpolyglots with knowledge of 10+ languages) and examined its response to the native language, non-native languages of varying proficiency, and unfamiliar languages. All language conditions engaged all areas of the language network relative to a control condition. Languages that participants rated as higher proficiency elicited stronger responses, except for the native language, which elicited a similar or lower response than a non-native language of similar proficiency. Furthermore, unfamiliar languages that were typologically related to the participants' high-to-moderate-proficiency languages elicited a stronger response than unfamiliar unrelated languages. The results suggest that the language network's response magnitude scales with the degree of engagement of linguistic computations (e.g. related to lexical access and syntactic-structure building). We also replicated a prior finding of weaker responses to native language in polyglots than non-polyglot bilinguals. These results contribute to our understanding of how multiple languages coexist within a single brain and provide new evidence that the language network responds more strongly to stimuli that more fully engage linguistic computations.

Indexed as

MultilingualismBrainBrain MappingHumansLanguageMagnetic Resonance ImagingfMRIlanguage networkmultilingualismpolyglotismproficiency

Identifiers

PMID38466812
PMCPMC10928488

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.