Bilinguals differ from monolinguals in attentional resource allocation during spoken language processing: pupillometry evidence
Bibliographic record
Abstract
Abstract Bilingual experience may enhance attentional control, but little work has addressed whether monolinguals and bilinguals differ in allocating attentional resources. Focusing on speech processing, we examined listening effort via pupillometry in English monolinguals and simultaneous bilinguals, while they listened to passages in a familiar or unfamiliar language. Results demonstrated similar pupil responses across conditions in bilinguals, yet monolinguals showed significantly larger pupil size when listening to the unfamiliar language than the familiar one. Further, more English exposure (especially a longer stay in an English-speaking family) correlated with smaller pupil size in the familiar language condition. Overall, our findings suggest that bilinguals tend to exhibit greater listening effort than monolinguals, and a more cognitively demanding situation (i.e., listening to an unknown language) requires more effort in monolinguals. With this, we broadened the scope of research on bilingual cognition and demonstrated that bilingualism affects attentional resource allocation in spoken language processing.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".