Automatic auditory discrimination of vowels in simultaneous bilingual and monolingual speakers as measured by the mismatch negativity (MMN).
Bibliographic record
Abstract
MMN responses reflect whether language users have developed long-term memory traces in response to phonemes and whether they are able to perceive small acoustic changes within speech sound categories. Subtle acoustic changes within phonemes are often irrelevant to monolingual perceivers, but can be crucial for bilingual perceivers if the acoustic change differentiates the phonemes of their two languages. In the present study, we investigated whether bilinguals are sensitive to such acoustic changes. We recorded MMN responses from monolingual (English, French) and simultaneous bilingual (English/French) adults using an auditory oddball paradigm in response to four vowels: English [u], French [u], French [y], and an acoustically-distinct (control) [y]. In line with previous findings, monolinguals were more sensitive to the phonemic status of the vowels than to the acoustic properties differentiating the sounds. Bilingual speakers revealed a different pattern; they demonstrated overall slower discrimination responses to all sounds, but showed almost equal sensitivity to phonemic and phonetic/acoustic differences. The results suggest that bilingual speakers exhibit a more flexible but less uniquely-specified perceptual pattern compared to monolingual speakers.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".