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Record W4395463160 · doi:10.1017/s136672892400021x

Executive function's structure in monolingual and bilingual adults using confirmatory factor analysis

2024· article· en· W4395463160 on OpenAlexafffund
Farzaneh Anjomshoae, Sandra A. Wiebe, Elena Nicoladis

Bibliographic record

VenueBilingualism Language and Cognition · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConfirmatory factor analysisPsychologyFactor (programming language)Function (biology)Cognitive psychologyLinguisticsNatural language processingStatisticsStructural equation modelingComputer scienceMathematics

Abstract

fetched live from OpenAlex

Abstract In processing their two languages, bilinguals have to selectively attend to the target language and reduce interference from the non-target language. This experience may have specific cognitive consequences on Executive Functions (EF) through bilingual language processing. Some studies found cognitive consequences in executive functioning skills. However, other studies did not replicate these findings or found a bilingual disadvantage. The aim of this study was to test for the cognitive consequences of bilingualism in EF among a large number of young adults using a latent variable approach, to rule out non-EF task differences as an explanation for inconsistency across studies. Also, we were interested in testing the EF structure using the Confirmatory Factor Analysis (CFA) approach. The results did not support a cognitive consequence of bilingualism and also the EF structure was the same for both groups. We discuss other possible variables that might contribute to the mixed results across studies.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.289
Teacher spread0.270 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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