How bilinguals perceive speech depends on which language they think they’re hearing.
Bibliographic record
Abstract
Bilinguals understand when the communication context calls for speaking a particular language and can switch from speaking one language to the other based on such conceptual knowledge. There is disagreement regarding whether conceptually-based language switching is also possible in the listening modality. For example, can bilingual listeners perceptually adjust to changes in pronunciation across languages based on their conceptual understanding of which language they’re currently hearing? We asked French- and Spanish-English bilinguals to identify nonsense monosyllables as beginning with /b/ or /p/, speech categories that French and Spanish speakers pronounce differently than English speakers. We conceptually cued each bilingual group to one of their two languages or the other by explicitly instructing them that the speech items were word onsets in that language, uttered by a native speaker thereof. Both groups adjusted their /b–p/ identification boundary in accordance with this conceptual cue to the language context. These results support a bilingual model permitting conceptually-based language selection on both the speaking and listening end of a communicative exchange.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".