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Record W4307567430 · doi:10.1017/s1366728922000657

ERP differences between monolinguals and bilinguals: The role of linguistic distance

2022· article· en· W4307567430 on OpenAlexafffund
Cassandra Morrison, Vanessa Taler

Bibliographic record

VenueBilingualism Language and Cognition · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsBruyèreUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsP3bNeuroscience of multilingualismPsychologyArabicTask (project management)Event-related potentialLinguisticsCognitionCognitive psychology

Abstract

fetched live from OpenAlex

Abstract A growing body of research suggests that bilingualism may afford benefits to certain aspects of cognitive functioning. Inconsistent findings may arise because of methodological differences within and across studies. One limitation is that studies often compare linguistically similar languages. The present study recorded brain activity (event-related potentials; ERPs) while English monolinguals, English–French bilinguals, and Arabic–English bilinguals completed an n-back task and a delayed matching-to-sample task. Group ERP differences were observed in the absence of behavioral differences. In the delayed matching-to-sample task, monolinguals exhibited smaller N2 amplitude compared to both bilingual groups, and smaller P3b amplitude compared to English–French bilinguals. In the n-back, English–French bilinguals displayed larger P3b amplitudes than monolinguals and Arabic–English bilinguals. P3b amplitude did not differ between Arabic–English bilinguals and monolinguals in either task. These results suggest that conflicting findings across studies may be due in part to the linguistic distance between the languages under study.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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