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Record W2144440751 · doi:10.1037/0278-7393.34.4.859

Cognitive control and lexical access in younger and older bilinguals.

2008· article· en· W2144440751 on OpenAlexafffund
Ellen Bialystok, Fergus I. M. Craik, Gigi Luk

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsBaycrest HospitalYork University
FundersCanadian Institutes of Health Research
KeywordsPsychologyCognitionWorking memoryTask (project management)Executive functionsNeuroscience of multilingualismLexical accessCognitive psychologyControl (management)MultilingualismElementary cognitive taskDevelopmental psychologyTask analysis

Abstract

fetched live from OpenAlex

Ninety-six participants, who were younger (20 years) or older (68 years) adults and either monolingual or bilingual, completed tasks assessing working memory, lexical retrieval, and executive control. Younger participants performed most of the tasks better than older participants, confirming the effect of aging on these processes. The effect of language group was different for each type of task: Monolinguals and bilinguals performed similarly on working memory tasks, monolinguals performed better on lexical retrieval tasks, and bilinguals performed better on executive control tasks, with some evidence for larger language group differences in older participants on the executive control tasks. These results replicate findings from individual studies obtained using only 1 type of task and different participants. The confirmation of this pattern in the same participants is discussed in terms of a suggested explanation of how the need to manage 2 language systems leads to these different outcomes for cognitive and linguistic functions.

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)0.000
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.056
GPT teacher head0.381
Teacher spread0.325 · 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

Citations917
Published2008
Admission routes2
Has abstractyes

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