Is there a bilingual advantage in auditory attention among children? A systematic review and meta-analysis of standardized auditory attention tests
Bibliographic record
Abstract
Abstract A wealth of research has investigated the effects of bilingualism on cognition, especially on executive function. Developmental studies reveal different cognitive profiles between monolinguals and bilinguals in (audio-)visual attention tasks, which might stem from their attention allocation differences. Yet, whether such distinction exists in the auditory domain alone is unknown. In this study, we compared differences in auditory attention, measured by standardized tests, between monolingual and bilingual children. A comprehensive literature search was conducted in three electronic databases: OVID Medline, OVID PsycInfo, and EBSCO CINAHL. Twenty studies using standardized tests to assess auditory attention in monolingual and bilingual participants aged less than 18 years were identified. We assessed the quality of these studies using a scoring tool for evaluating primary research. For statistical analysis, we pooled the effect size in a random-effects meta-analytic model, where between-study heterogeneity was quantified using the I 2 statistic. No substantial publication bias was observed based on the funnel plot. Further, meta-regression modelling suggests that test measure (accuracy vs. response times) significantly affected the studies’ effect sizes whereas other factors (e.g., participant age, stimulus type) did not. Specifically, studies reporting accuracy observed marginally greater accuracy in bilinguals ( g = 0.10), whereas those reporting response times indicated faster latency in monolinguals ( g = -0.34). There was little difference between monolingual and bilingual children’s performance on standardized auditory attention tests. We also found that studies tend to include a wide variety of bilingual children but report limited language background information of the participants. This, unfortunately, limits the potential theoretical contributions of the reviewed studies. Recommendations to improve the quality of future research are discussed.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".