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Record W2159863694 · doi:10.1017/s1366728911000010

Is there a relation between onset age of bilingualism and enhancement of cognitive control?

2011· article· en· W2159863694 on OpenAlexaff
Gigi Luk, Eric de, Ellen Bialystok

Bibliographic record

VenueBilingualism Language and Cognition · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsYork UniversityBaycrest Hospital
Fundersnot available
KeywordsNeuroscience of multilingualismPsychologyCognitionLanguage proficiencyAge of AcquisitionContrast (vision)Significant differenceDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Young English-speaking monolingual and bilingual adults were examined for English proficiency, language use history, and performance on a flanker task. The bilinguals, who were about twenty years old, were divided into two groups (early bilinguals and late bilinguals) according to whether they became actively bilingual before or after the age of ten years. Early bilinguals and monolinguals demonstrated similar levels of English proficiency, and both groups were more proficient in English than late bilinguals. In contrast, early bilinguals produced the smallest response time cost for incongruent trials (flanker effect) with no difference between monolinguals and late bilinguals. Moreover, across the whole sample of bilinguals, onset age of active bilingualism was negatively correlated with English proficiency and positively correlated with the flanker effect. These results suggest a gradient in which more experience in being actively bilingual is associated with greater advantages in cognitive control and higher language proficiency.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.297
Teacher spread0.248 · 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

Citations416
Published2011
Admission routes1
Has abstractyes

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