Phasic pupillary responses reveal differential engagement of attentional control in bilingual spoken language processing
Bibliographic record
Abstract
Language processing is cognitively demanding, requiring attentional resources to efficiently select and extract linguistic information as utterances unfold. Previous research has associated changes in pupil size with increased attentional effort. However, it is unknown whether the behavioral ecology of speakers may differentially affect engagement of attentional resources involved in conversation. For bilinguals, such an act potentially involves competing signals in more than one language and how this competition arises may differ across communicative contexts. We examined changes in pupil size during the comprehension of unilingual and codeswitched speech in a richly-characterized bilingual sample. In a visual-world task, participants saw pairs of objects as they heard instructions to select a target image. Instructions were either unilingual or codeswitched from one language to the other. We found that only bilinguals who use each of their languages in separate communicative contexts and who have high attention ability, show differential attention to unilingual and codeswitched speech. Bilinguals for whom codeswitching is common practice process unilingual and codeswitched speech similarly, regardless of attentional skill. Taken together, these results suggest that bilinguals recruit different language control strategies for distinct communicative purposes. The interactional context of language use critically determines attentional control engagement during language processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".