Post‐conflict slowing effects in monolingual and bilingual children
Bibliographic record
Abstract
Previous research has shown that bilingual children outperform their monolingual peers on a wide variety of tasks measuring executive functions (EF). However, recent failures to replicate this finding have cast doubt on the idea that the bilingual experience leads to domain-general cognitive benefits. The present study explored the role of disengagement of attention as an explanation for why some studies fail to produce this result. Eighty children (40 monolingual, 40 bilingual) who were 7 years old performed a task-switching experiment. In the pure blocks, three simple non-conflict tasks were performed in which children responded by pressing one of two response keys. In the conflict block, occasional bivalent stimuli appeared and created conflict because the irrelevant dimension was mapped to the incorrect response key. The results showed that these bivalent stimuli affected subsequent performance in the conflict block. For monolinguals, the effect of conflict was found for up to 12 trials after the appearance of the bivalent stimulus, but for bilinguals the effect disappeared after only two trials. The results are interpreted as evidence for faster disengagement of attention by bilingual children. Most studies examining EF in monolingual and bilingual children do not examine trial-by-trial adjustments following conflict, but these are essential considerations because relevant processing differences are masked when analyses are applied to data averaged across entire blocks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".