Asymmetries in vowel perception: Effects of formant convergence and category “goodness”
Bibliographic record
Abstract
The mechanisms underlying directional asymmetries in vowel perception have been the subject of considerable debate. One account—the Natural Referent Vowel (NRV) framework—suggests that asymmetries reflect a language-universal perceptual bias, such that listeners are predisposed to attend to vowels with greater formant convergence (Polka & Bohn, 2011). A second (but not mutually exclusive) account—the Native Language Magnet (NLM) theory—suggests that asymmetries reflect an experience-dependent language-specific bias favoring “good” exemplars of native language vowel categories (Kuhl, 1993). We tested the above hypotheses by investigating whether listeners, from different language backgrounds, display asymmetries influenced by formant proximity and/or language experience. Specifically, we examined monolingual English and French listeners’ performance in a within-category AX vowel discrimination task, using variants of /u/ that systematically differed in both their degree of formant proximity (between F1 and F2) and category “goodness” judgments. Results revealed asymmetries that pattern as predicted by NRV when pairs of /u/ tokens exhibited a relatively larger difference in their F1-F2 convergence patterns, and as predicted by NLM when the pairs of /u/ tokens exhibited a relatively smaller difference in their F1-F2 convergence patterns. These findings suggest that language-universal perceptual biases and specific language experience interact to shape vowel perception.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".