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Record W4200126078 · doi:10.1017/s1366728921000912

Effect of bilingualism on infants’ cognitive flexibility

2021· article· en· W4200126078 on OpenAlexafffund
Diane Poulin‐Dubois, Cassandra Neumann, Sandra Masoud, Adina Gazith

Bibliographic record

VenueBilingualism Language and Cognition · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCognitive flexibilityNeuroscience of multilingualismFlexibility (engineering)CognitionExecutive functionsTask (project management)PsychologyTask switchingCognitive psychologyDevelopmental psychologyWorking memoryNeuroscience

Abstract

fetched live from OpenAlex

Abstract Research suggests that bilinguals often outperform monolinguals on tasks that tap into executive functions, such as those requiring conflict resolution and cognitive flexibility. Recently, better attentional control has been detected in infants as young as 6 months, thereby providing a possible basis for a cognitive benefit before language production. The goal of the present study was to examine if cognitive flexibility is more advanced in bilingual infants. A detour reaching task assessing conflict resolution, a delayed response task assessing shifting, and a multiple location task assessing maintaining, were administered to 17-month-old infants. The main findings revealed that being bilingual did not improve performance on any of the executive function tasks. Furthermore, current exposure to a second language or language proficiency did not impact executive functioning. We conclude that a bilingual advantage in cognitive flexibility may not be present before children have enough experience in code switching.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.023
GPT teacher head0.327
Teacher spread0.304 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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