Individual Differences in Proactive and Reactive Control in Bilinguals
Bibliographic record
Abstract
This study investigated individual differences in bilinguals’ use of proactive and reactive control processes during an executive control task (the AX-CPT) in relation to aspects of the bilingual experience (e.g., second language proficiency). Participants were presented with cue-target letter pairs, one letter at a time (AX, AY, BX, or BY; B and Y are any letter other than A or X) and were instructed to press the “yes” button for AX pairs and the “no” button for any other pair. They completed three blocks which varied in terms of the most frequent trial type (AX-70% vs. AY-70% vs. BX-70%). Event-related brain potentials (ERPs) were recorded from 15 young adult bilinguals during the AX-CPT. The N2, an ERP related to conflict detection, was analyzed in conjunction with behavioural performance. \n \nIndividual variations in cognitive control strategy were differentially associated with aspects of bilingualism in the AX-70 and AY-70 blocks. In the AX-70 block, greater engagement of proactive control was associated with shorter overall reaction times (RTs), lower accuracy, and enhanced conflict detection. In the AY-70 block, a proactive strategy was associated with lower accuracy, but similar RTs compared to a reactive strategy. \n \nDifferent patterns of association were found between self-reported language-switching behaviours and cognitive control strategy in the AX-70 block compared to the AY-70 block. \n \nThe results support the idea of individual differences in the relative use of proactive and reactive mechanisms in bilinguals. These differences were related to aspects of language-switching which is an important source of interindividual variability among bilinguals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".