Language athletes: Dual-language code-switchers exhibit inhibitory control advantages
Bibliographic record
Abstract
Recent studies have begun to examine bilingual cognition from more nuanced, experienced-based perspectives. The present study adds to this body of work by investigating the potential impact of code-switching on bilinguals’ inhibitory control abilities. Crucially, our bilingual participants originated from a predominantly dual-language environment, the interactional context which is believed to require (and therefore, potentially train) cognitive control processes related to goal-monitoring and inhibition. As such, 266 French Canadian bilinguals completed an online experiment wherein they were asked to complete a domain-general (Flanker) and a language-specific (bilingual Stroop) inhibitory control task, as well as extensive demographic and language background questionnaires. Stepwise multiple regressions (including various potential demographic and linguistic predictors) were conducted on the participants’ Flanker and Stroop effects. The results indicated that the bilinguals’ propensity to code-switch consistently yielded significant positive (but unidirectional) inhibitory control effects: dual-language bilinguals who reported more habitual French-to-English switching exhibited better goal-monitoring and inhibition abilities. For the language-specific task, the analysis also revealed that frequent unintentional code-switching may mitigate these inhibition skills. As such, the findings demonstrate that dual-language code-switchers may experience inhibitory control benefits, but only when their switching is self-reportedly deliberate. We conclude that the bilinguals’ interactional context is thus of primary importance, as the dual-language context is more conducive to intentional code-switching. Overall, the current study highlights the importance of considering individualistic language experience when it comes to examining potential bilingual executive functioning advantages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".