Predicting asymmetries in vowel perception: Formant convergence succeeds where peripherality fails
Bibliographic record
Abstract
Vowel discrimination is often asymmetric such that discriminating the same vowel contrast is easier in one direction compared to the opposite direction. According to the Natural Referent Vowel (NRV) framework, these asymmetries reveal a perceptual bias favoring acoustic vowel signals produced with more extreme vocalic gestures, which act as natural referent vowels. The NR vowel within a contrast typically falls in a more peripheral location within articulatory/acoustic vowel space (defined by F1 and F2) and with F1 and F2 in closer proximity. However, these properties do not always align, as in the case of the /e/-/Ø/ contrast. We here compared findings across three studies where asymmetries were observed during discrimination of this contrast. Peripherality predicts an asymmetry such that perception would be better in the /Ø/□/e/ direction. However, all three studies showed better performance in the opposite direction, which also aligns with the prediction based on formant convergence (derived from reported acoustic measures for each stimulus set). These findings suggest that this perceptual bias is shaped by formant convergence, rather than peripherality per se, which is presumably tied to the extent of vocal-tract constriction. A follow-up study will obtain articulographic recordings of these vocalic gestures to quantify this articulatory-acoustic relation.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".