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Record W3016960923 · doi:10.1177/0956797620903113

Bilingualism Affords No General Cognitive Advantages: A Population Study of Executive Function in 11,000 People

2020· article· en· W3016960923 on OpenAlexafffund
Emily S. Nichols, Conor J. Wild, Bobby Stojanoski, Michael E. Battista, Adrian M. Owen

Bibliographic record

VenuePsychological Science · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWestern University
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsNeuroscience of multilingualismPsychologyCognitionExecutive functionsConfoundingPopulationCognitive psychologyMultilingualismTest (biology)Developmental psychology

Abstract

fetched live from OpenAlex

Whether acquiring a second language affords any general advantages to executive function has been a matter of fierce scientific debate for decades. If being bilingual does have benefits over and above the broader social, employment, and lifestyle gains that are available to speakers of a second language, then it should manifest as a cognitive advantage in the general population of bilinguals. We assessed 11,041 participants on a broad battery of 12 executive tasks whose functional and neural properties have been well described. Bilinguals showed an advantage over monolinguals on only one test (whereas monolinguals performed better on four tests), and these effects all disappeared when the groups were matched to remove potentially confounding factors. In any case, the size of the positive bilingual effect in the unmatched groups was so small that it would likely have a negligible impact on the cognitive performance of any individual.

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.054
GPT teacher head0.374
Teacher spread0.319 · 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

Citations201
Published2020
Admission routes2
Has abstractyes

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