Directional asymmetries reveal a universal bias in adult vowel perception
Bibliographic record
Abstract
Research on cross-language vowel perception in both infants and adults has shown that for many vowel contrasts, discrimination is easier when the same pair of vowels is presented in one direction compared to the reverse direction. According to one account, these directional asymmetries reflect a universal bias favoring "focal" vowels (i.e., vowels whose adjacent formants are close in frequency, which concentrates acoustic energy into a narrower spectral region). An alternative, but not mutually exclusive, account is that such effects reflect an experience-dependent bias favoring prototypical instances of native-language vowel categories. To disentangle the effects of focalization and prototypicality, the authors first identified a certain location in phonetic space where vowels were consistently categorized as /u/ by both Canadian-English and Canadian-French listeners, but that nevertheless varied in their stimulus goodness (i.e., the best Canadian-French /u/ exemplars were more focal compared to the best Canadian-English /u/ exemplars). In subsequent AX discrimination tests, both Canadian-English and Canadian-French listeners performed better at discriminating changes from less to more focal /u/'s compared to the reverse, regardless of variation in prototypicality. These findings demonstrate a universal bias favoring vowels with greater formant convergence that operates independently of biases related to language-specific prototype categorization.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".