Asymmetries in the mismatch negativity response to vowels by French, English, and bilingual adults: Evidence for a language-universal bias.
Bibliographic record
Abstract
In infants, discrimination of a vowel change presented in one direction is often significantly better compared to the same change presented in the reverse direction. These directional asymmetries reveal a language-universal bias favoring vowels with extreme articulatory-acoustic properties (peripheral in F1/F2 vowel space). In adults, asymmetries are observed for non-native but not for native vowel contrasts. To examine neurophysiological correlates of these asymmetries we recorded MMN responses from monolingual (English, French) and simultaneous bilingual (English/French) adults using an oddball paradigm with four vowels: English [u], French [u], French [y], and an acoustically-distinct [y]. All vowels were tested in four conditions with each vowel serving as deviant and standard. Within each vowel pair, MMN responses were larger and earlier when the deviant vowel was more peripheral. This pattern was consistent across the language groups and was observed for within-category (within [u]; within [y]) and for cross-category vowel pairs ([u] versus [y]). Findings indicate that a bias favoring peripheral vowels is retained in the neural pre-attentive processing of vowels in adults. As in infant behavioral findings, this bias is independent of the functional status of the vowel pair. Implications for the natural referent vowel model [Polka and Bohn (2003)] will be discussed.
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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 | 0.000 | 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".