Pushing Boundaries: Bilinguals’ Phoneme Perception When Cued by Real Words
Bibliographic record
Abstract
Phoneme perception varies across languages, as listeners of different languages use the same phonetic cues differently to determine which phoneme they are hearing. This raises the question of how bilinguals perceive phonemes in each of their languages. Previous research has found that bilinguals are able to perceive phonemes in a language-specific manner based on cues such as voice onset time, but this work has mostly tested listeners’ perception of syllables and non-words. This pre-registered study examined bilingual adults’ phoneme perception while hearing real, full words in both of their languages. Bilinguals’ perception shifted to some degree based on what language they were hearing, supporting the idea of language-specific perception. However, by far the greatest influence on perception was lexical knowledge, whereby bilinguals were more likely to report hearing the sound that resulted in a real word regardless of language context (e.g., reporting they heard “puppy” even when it was phonetically realized as “buppy”, also known as the Ganong effect). These findings highlight how more ecologically valid studies can enrich our understanding of bilinguals’ phoneme perception.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".