Bilingualism and executive attention: Evidence from studies of proactive and reactive control.
Bibliographic record
Abstract
According to some accounts, the bilingual advantage is most pronounced in the domain of executive attention rather than inhibition and should therefore be more easily detected in conflict adaptation paradigms than in simple interference paradigms. We tested this idea using two conflict adaptation paradigms, one that elicits a list-wide proportion-congruent effect and one that elicits an item-specific proportion-congruent effect. In both cases, the relevant finding is that congruency effects are reduced when the proportion of congruent to incongruent items is smaller. These effects are validated measures of proactive and reactive control, respectively, and are aspects of executive attention known to be associated with individual differences in working memory capacity. We reasoned that if bilingualism affects executive attention in a similar way as does working memory capacity, indices of proactive and reactive control should be comparably associated with continuous variation in language status and working memory capacity. In two experiments, we replicated previous findings that working memory capacity is associated with variation in congruency effects (suggesting greater reliance on proactive control). In contrast, language status had no consistent association with performance, save for a hint that bilingualism may be associated with greater reliance on reactive control. Thus, the bilingual advantage may exist, but not in proactive control or any other aspects of executive attention that have been proposed thus far. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".