Evidence map›Paper›PMID 33346131›Full record

ArticleNeuroImage2021

Neural representation of linguistic feature hierarchy reflects second-language proficiency.

Giovanni M Di Liberto, Jingping Nie, Jeremy Yeaton, Bahar Khalighinejad, Shihab A Shamma, Nima Mesgarani

Abstract read
In one paragraph

Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Review
  2. Article
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  5. Hierarchical dynamic coding coordinates speech comprehension in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
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  11. Review
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  14. Linguistic modulation of the neural encoding of phonemes.Cerebral cortex (New York, N.Y. : 1991) · 2024
    Article
  15. Neural Representations of Non-native Speech Reflect Proficiency and Interference from Native Language Knowledge.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  16. Article
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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

6 authors.

Giovanni M Di LibertoLaboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, 75005 Paris, France. Electronic address: diliberg@tcd.ie.
Jingping NieDepartment of Electrical Engineering, Columbia University, New York, NY, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, United States.
Jeremy YeatonLaboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, 75005 Paris, France; Laboratoire de Psychologie Cognitive, UMR 7290, CNRS, France. Aix-Marseille Université, France.
Bahar KhalighinejadDepartment of Electrical Engineering, Columbia University, New York, NY, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, United States.
Shihab A ShammaLaboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, 75005 Paris, France; Institute for Systems Research, Electrical and Computer Engineering, University of Maryland, College Park, USA.
Nima MesgaraniDepartment of Electrical Engineering, Columbia University, New York, NY, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, United States. Electronic address: nima@ee.columbia.edu.

Funding

Neurophysiology of robust speech perception in human superior temporal gyrusR01DC014279 · NIDCD · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI Nima MESGARANI · 2015 to 2026
$5.3M
Characterizing the temporal processing of speech in the human auditory cortexR01DC018805 · NIDCD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FLINKER, ADEEN, MESGARANI, NIMA · 2021 to 2025
$3.3M
Neurophysiology of auditory emotion recognition in the human brainR21MH114166 · NIMH · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · PI MEHTA, ASHESH DINESH · 2017 to 2018
$456k
NIDCD NIH HHS R01 DC014279NIDCD NIH HHS R01 DC018805NIMH NIH HHS R21 MH114166
6 · The paper itself

Abstract

Acquiring a new language requires individuals to simultaneously and gradually learn linguistic attributes on multiple levels. Here, we investigated how this learning process changes the neural encoding of natural speech by assessing the encoding of the linguistic feature hierarchy in second-language listeners. Electroencephalography (EEG) signals were recorded from native Mandarin speakers with varied English proficiency and from native English speakers while they listened to audio-stories in English. We measured the temporal response functions (TRFs) for acoustic, phonemic, phonotactic, and semantic features in individual participants and found a main effect of proficiency on linguistic encoding. This effect of second-language proficiency was particularly prominent on the neural encoding of phonemes, showing stronger encoding of "new" phonemic contrasts (i.e., English contrasts that do not exist in Mandarin) with increasing proficiency. Overall, we found that the nonnative listeners with higher proficiency levels had a linguistic feature representation more similar to that of native listeners, which enabled the accurate decoding of language proficiency. This result advances our understanding of the cortical processing of linguistic information in second-language learners and provides an objective measure of language proficiency.

Indexed as

MultilingualismAdolescentAdultBrainComprehensionElectroencephalographyFemaleHumansLanguageMaleMiddle AgedPhoneticsSpeech PerceptionYoung Adult

Identifiers

PMID33346131
PMCPMC8527895

What OpenQuestion holds

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LicenceCC BY
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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.