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Record W2314051384 · doi:10.1037/a0037254

Effects of bilingualism and aging on executive function and working memory.

2014· article· en· W2314051384 on OpenAlexafffund
Ellen Bialystok, Gregory J. Poarch, Lin Luo, Fergus I. M. Craik

Bibliographic record

VenuePsychology and Aging · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsBaycrest HospitalYork University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsStroop effectPsychologyWorking memoryNonverbal communicationNeuroscience of multilingualismTask (project management)Executive functionsCognitionDevelopmental psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Two studies are reported in which younger and older monolingual and bilingual adults performed executive function tasks. In Study 1, 130 participants performed a Stroop task and bilinguals in both age groups showed less interference than monolinguals with a greater benefit for older adults. In Study 2, 108 participants performed a complex working memory task based on verbal or nonverbal stimuli. Bilinguals showed less interference than monolinguals, with a larger bilingual advantage in the older adult group and in the nonverbal task. Together, these results show that bilingual advantages in executive function depend on characteristics of the participants and features of the tasks, with larger effects found for older than younger adults and for complex tasks using nonverbal material.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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

Citations247
Published2014
Admission routes2
Has abstractyes

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