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Record W2509698766 · doi:10.1080/17470218.2016.1221435

The Relationship between Bilingualism and Selective Attention in Young Adults: Evidence from an Ambiguous Figures Task

2016· article· en· W2509698766 on OpenAlexafffund
Ashley Chung-Fat-Yim, Geoff B. Sorge, Ellen Bialystok

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyNeuroscience of multilingualismCognitive psychologySelective attentionTask (project management)Function (biology)Mechanism (biology)Variety (cybernetics)Executive functionsCognitionDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

Previous research has shown that bilinguals outperform monolinguals on a variety of tasks that have been described as involving executive functioning, but the precise mechanism for those effects or a clear definition for "executive function" is unknown. This uncertainty has led to a number of studies for which no performance difference between monolingual and bilingual adults has been detected. One approach to clarifying these issues comes from research with children showing that bilinguals were more able than their monolingual peers to perceive both interpretations of an ambiguous figure, an ability that is more tied to a conception of selective attention than to specific components of executive function. The present study extends this notion to adults by assessing their ability to see the alternative image in an ambiguous figure. Bilinguals performed this task more efficiently than monolinguals by requiring fewer cues to identify the second image. This finding has implications for the role of selective attention in performance differences between monolinguals and bilinguals.

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)0.006
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
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.385
Teacher spread0.330 · 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

Citations53
Published2016
Admission routes2
Has abstractyes

Explore more

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