Bilinguals mind their language (mode): Vowel perception patterns of simultaneous bilingual and monolingual speakers.
Bibliographic record
Abstract
It is well-established that the speech perception abilities of monolingual speakers are highly tuned to the sounds of their native language, and that this language specificity affects how monolingual speakers distinguish the sounds of a non-native language. The present study addressed how the speech perception skills of simultaneous bilingual speakers, who are native speakers of two languages, may be affected by control of active language mode. We tested monolingual (English and French) and simultaneous bilingual (English/French) adults in an identification and rating task with 42 vowels along a continuum from a high back rounded vowel (/u/) to a high front rounded vowel (/y/) that are both phonemic in French, with only the back vowel represented in English. Bilinguals completed the task in three language modes: English, French, and bilingual. As expected, monolingual speakers demonstrated a language-specific perceptual pattern for the vowels. Bilingual participants displayed different perceptual patterns in each active language mode to accommodate the vowel categories relevant in the target language. These findings indicate that simultaneous bilinguals rely on a finely detailed perceptual space and are flexible as they adapt their perception to different language 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".