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Record W3091911202 · doi:10.1017/s1366728920000516

Bilinguals benefit from semantic context while perceiving speech in noise in both of their languages: Electrophysiological evidence from the N400 ERP

2020· article· en· W3091911202 on OpenAlexaff
Kristina Coulter, Annie C. Gilbert, Shanna Kousaie, Shari R. Baum, Vincent L. Gracco, Denise Klein, Debra Titone, Natalie A. Phillips

Bibliographic record

VenueBilingualism Language and Cognition · 2020
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityCentre for Research on Brain Language and Music
Fundersnot available
KeywordsN400Context (archaeology)Speech perceptionPsychologyNeuroscience of multilingualismSemantic memorySemantics (computer science)Cognitive psychologyTask (project management)PerceptionEvent-related potentialComputer scienceCognition

Abstract

fetched live from OpenAlex

Abstract Although bilinguals benefit from semantic context while perceiving speech-in-noise in their native language (L1), the extent to which bilinguals benefit from semantic context in their second language (L2) is unclear. Here, 57 highly proficient English–French/French–English bilinguals, who varied in L2 age of acquisition, performed a speech-perception-in-noise task in both languages while event-related brain potentials were recorded. Participants listened to and repeated the final word of sentences high or low in semantic constraint, in quiet and with a multi-talker babble mask. Overall, our findings indicate that bilinguals do benefit from semantic context while perceiving speech-in-noise in both their languages. Simultaneous bilinguals showed evidence of processing semantic context similarly to monolinguals. Early sequential bilinguals recruited additional neural resources, suggesting more effective use of semantic context in L2, compared to late bilinguals. Semantic context use was not associated with bilingual language experience or working memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.291
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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