Maintainance vs. “loss” of the perceptual bias favoring natural reference vowels
Bibliographic record
Abstract
Previous infant vowel discrimination studies (Polka & Bohn, 2003) have shown that babies are perceptually biased to favor vowels with more extreme articulations such that a change from a more peripheral vowel (“natural reference vowel” - NRV) to a vowel located more centrally in the vowel space is less discriminable than a change presented in the reverse direction. Recent studies of adult vowel perception suggest that the universal bias favoring NRVs is maintained in the absence of specific language experience and but “lost” if native or non-native language experience causes a reset of the perceptual default favoring NRVs. This presentation reports on vowel discrimination experiments which examined whether absence or presence of specific vowel experience is related to maintenance or loss of this perceptual bias in Danish infants (age 6-12 months). For two Danish vowel contrasts, asymmetries were observed for younger infants, but not for infants older than 10 months. For an English vowel contrast that does not exist in Danish, the asymmetry was maintained in infants up to 12 months and in adult Danish listeners. Results confirm the assumption that the perceptual bias favoring NRVs is maintained throughout the lifespan unless specific vowel experience causes on override. [Work supported by the Danish Research Council for the Humanities.]
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".