MétaCan
Menu
Back to cohort
Record W4307392181 · doi:10.1017/s1366728922000645

Subcortical plasticity and enhanced neural synchrony in multilingual adults

2022· article· en· W4307392181 on OpenAlexaff
Zahra Jafari, Caroline Villeneuve, Jordon Thompson, Amineh Koravand

Bibliographic record

VenueBilingualism Language and Cognition · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAudiologyNeuroscience of multilingualismMultilingualismPsychologyNeuroplasticityHyperacusisLatency (audio)Auditory cortexMedicineNeuroscienceHearing lossComputer science

Abstract

fetched live from OpenAlex

Abstract Whereas growing evidence supports the advantages of bilingualism for brain structure and function, no study has shown multilingual-related neuroplasticity in response to speech stimuli at the subcortical level. To investigate the impact of multilingualism on subcortical auditory processing, the speech auditory evoked response (speech-ABR) was recorded on 35 young adults. The multilingual group completed the language experience and proficiency questionnaire (LEAP-Q). The results were that multilingual participants demonstrated evidence of enhanced neural timing processing, including a shorter wave D latency and the V-A duration, and a sharper V-A slope compared to the monolinguals in silence. In the noise condition, the speech-ABR measures degraded in most components, and no significant difference was observed between the two groups. The association between the total proficiency score and several subcortical responses was significant. This shows subcortical evidence of stronger neural synchronization in multilinguals relative to monolinguals, correlated with the self-report of multilingual experience.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.000

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 head0.019
GPT teacher head0.283
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBilingualism Language and CognitionSame topicNeuroscience and Music PerceptionFrench-language works237,207