Navigating the bilingual cocktail party: a critical role for listeners’ L1 in the linguistic aspect of informational masking
Bibliographic record
Abstract
Abstract Cocktail party environments require listeners to tune in to a target voice while ignoring surrounding speakers. This presents unique challenges for bilingual listeners who have familiarity with several languages. Our study recruited English-French bilinguals to listen to a male target speaking French or English, masked by two female voices speaking French, English or Tamil, or by speech-shaped noise, in a fully factorial design. Listeners struggled most with L1 maskers and least with foreign maskers. Critically, this finding held regardless of the target language (L1 or L2) challenging theories about the linguistic component of informational masking, which contrary to our results predicts stronger interference with greater target-to-masker similarity such as L2 vs L2 compared to L2 vs L1. Our findings suggest that the listener’s familiarity with the masker language is an important source of informational masking in multilingual environments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".