On the time course of exogenous cueing effects in bilinguals: Higher proficiency in a second language is associated with more rapid endogenous disengagement
Bibliographic record
Abstract
Previous investigations have demonstrated a bilingual advantage on various aspects of executive control. It remains unclear how the language proficiency of bilinguals might relate to the mechanisms involved in attentional disengagement. In the present investigation, we tested the hypothesis that high bilingual proficiency would lead to a more rapid endogenous disengagement of attention from task-irrelevant peripheral cues. We predicted that more rapid attentional disengagement would result in an earlier appearance of inhibition of return (IOR). In this study Hindi-English bilinguals who differed in their L2 (English) proficiency participated in a target detection task. Visual targets were preceded by uninformative peripheral cues at various stimulus onset asynchronies (SOAs) allowing for us to visualize the time course of cue-related facilitation and inhibition. High-proficient Hindi-English bilinguals showed an earlier appearance of IOR than did low-proficient bilinguals, suggesting increased efficiency in disengagement of attention from task-irrelevant inputs. Furthermore, consistent with the "global" advantage that characterizes bilinguals in many tasks, the high-proficient group outperformed low-proficient bilinguals in overall reaction time.
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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".