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Record W2963374180 · doi:10.3389/fpsyg.2019.01839

Training in Language Switching Facilitates Bilinguals’ Monitoring and Inhibitory Control

2019· article· en· W2963374180 on OpenAlexaff
Cong Liu, Chin‐Lung Yang, Lu Jiao, John W. Schwieter, Xun Sun, Ruiming Wang

Bibliographic record

VenueFrontiers in Psychology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWilfrid Laurier University
FundersNational Social Science Fund of China
KeywordsInhibitory controlPsychologyControl (management)CognitionContext (archaeology)SaccadeCognitive psychologyCognitive trainingTraining (meteorology)Computer scienceEye movementNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

In the present study, we use a training design in two experiments to examine whether bilingual language switching facilitates two components of cognitive control, namely monitoring and inhibitory control. The results of Experiment 1 showed that training in language switching reduced mixing costs and the anti-saccade effect among bilinguals. In Experiment 2, the findings revealed a greater decrease of mixing costs and a smaller decrease of the anti-saccade effect from pre- to post-training for the language switching training group compared to the second language training group. Overall, the results suggest that extensive exercise in monitoring and inhibitory control in an experimental setting may enhance the corresponding components of cognitive control. We discuss these findings in the context of the relationship between bilingual language control and executive control.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.318
Teacher spread0.292 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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