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Record W4280652493 · doi:10.3390/languages7020122

Testing the Bilingual Cognitive Advantage in Toddlers Using the Early Executive Functions Questionnaire

2022· article· en· W4280652493 on OpenAlexaff
Kayla Beaudin, Diane Poulin‐Dubois

Bibliographic record

VenueLanguages · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
Fundersnot available
KeywordsExecutive functionsCognitive flexibilityWorking memoryPsychologyCognitionMultilingualismDevelopmental psychologyFlexibility (engineering)Cognitive psychologyExecutive dysfunctionNeuropsychologyNeuroscience

Abstract

fetched live from OpenAlex

The present study aims to assess differences in executive functioning between monolingual and multilingual 23-month-old toddlers, both when dichotomizing multilingualism and assessing it on a continuum. It is hypothesized that multilinguals, individuals with greater non-dominant language exposure, and individuals with more translation equivalents, would perform better in the following domains: response inhibition, attentional flexibility, and regulation. No differences are expected for working memory. The Early Executive Functions Questionnaire, a newly developed parental report, is used to measure the four executive functions of interest. Multilinguals and individuals with greater non-dominant language exposure have significantly higher response inhibition; however, no differences are noted for any other executive function. Additionally, no associations between translation equivalents and executive functioning are found. Post-hoc analyses reveal that non-dominant language production had a positive correlation with working memory. The present findings support the notion of a domain-specific cognitive advantage for multilingual toddlers.

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)0.003
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.329
Teacher spread0.297 · 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

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