Attention during visual search: The benefit of bilingualism
Bibliographic record
Abstract
AIMS AND OBJECTIVES/PURPOSE/RESEARCH QUESTIONS: Following reports showing bilingual advantages in executive control (EC) performance, the current study investigated the role of selective attention as a foundational skill that might underlie these advantages. DESIGN/METHODOLOGY/APPROACH: Bilingual and monolingual young adults performed a visual search task by determining whether a target shape was present amid distractor shapes. Task difficulty was manipulated by search type (feature or conjunction) and by the number and discriminability of the distractors. In feature searches, the target (e.g., green triangle) differed on a single dimension (e.g., color) from the distractors (e.g., yellow triangles); in conjunction searches, two types of distractors (e.g., pink circles and turquoise squares) each differed from the target (e.g., turquoise circle) on a single but different dimension (e.g., color or shape). DATA AND ANALYSIS: Reaction time and accuracy data from 109 young adults (53 monolinguals and 56 bilinguals) were analyzed using a repeated-measures analysis of variance. Group membership, search type, number and discriminability of distractors were the independent variables. FINDINGS/CONCLUSIONS: Participants identified the target more quickly in the feature searches, when the target was highly discriminable from the distractors and when there were fewer distractors. Importantly, although monolinguals and bilinguals performed equivalently on the feature searches, bilinguals were significantly faster than monolinguals in identifying the target in the more difficult conjunction search, providing evidence for better control of visual attention in bilinguals. ORIGINALITY: Unlike previous studies on bilingual visual attention, the current study found a bilingual attention advantage in a paradigm that did not include a Stroop-like manipulation to set up false expectations. SIGNIFICANCE/IMPLICATIONS: Thus, our findings indicate that the need to resolve explicit conflict or overcome false expectations is unnecessary for observing a bilingual advantage in selective attention. Observing this advantage in a fundamental skill suggests that it may underlie higher order bilingual advantages in EC.
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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 | 0.002 |
| 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 | 0.001 | 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".