Factors shaping vowel perception biases in adults
Bibliographic record
Abstract
Vowel discrimination is often asymmetric such that discriminating the same vowel pair is easier in one direction compared to the opposite direction. The Natural Referent Vowel framework interprets these directional asymmetries as a universal bias favoring “focal” vowels (i.e., vowels with prominent spectral peaks formed by the convergence of adjacent formants). The Native Language Magnet model instead interprets asymmetries in terms of a language-specific bias due to distortion of perceptual space around native language vowel prototypes. To test these competing views, Masapollo et al. (2017) compared English- and French-speaking adults’ discrimination of synthetic /u/ variants; this was informative because English /u/ is naturally less focal than French /u/. Their findings revealed asymmetries to be predicted by focalization only; although stimulus limitations may explain the lack of prototype effects. Here, we synthesized a more refined series of vowel stimuli systematically varying in smaller psychophysical steps around the English /u/ and French /u/ prototypes to augment the measurement of focalization and prototype effects. Native English speakers completed a category goodness-rating task followed by an AX-discrimination task using these new variants. Results indicated effects of both focalization and prototype, which are moderated by the size of acoustic intervals along the stimulus series.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".