Voice Onset Time Imitation in Teens Versus Adults
Bibliographic record
Abstract
PURPOSE: We compare teens' and adults' imitation of sentences with shortened and lengthened voice onset time (VOT), in order to test whether purported age-based advantages in phonetic acquisition may be due to differences in imitative ability. METHOD: = 31) completed an explicit imitation and discrimination task on pairs of sentences characterized by canonical and manipulated (shortened or lengthened) VOT. We assessed extent of imitation using two acoustic metrics (ΔVOT and Proximity), accuracy on the discrimination task, and correlations between imitation and perception. RESULTS: Both age groups modified VOT when imitating stimuli with both lengthened and shortened VOT. Adults, however, showed significantly more lengthening than teens (i.e., higher ΔVOT), as well as VOT values that were slightly but significantly closer to the target stimulus values (i.e., lower proximity). Both age groups showed above-chance discrimination accuracy, and a significant relationship between individual perception and production performance was found for lengthened-VOT sentences. CONCLUSIONS: imitation based on both acoustic metrics. Both age groups showed robust imitation of VOT manipulations in both directions, in contrast to previous work showing lack of imitation for shortened VOT. Extent of imitation was predicted by individual perceptual performance, but only to a limited degree, underscoring the importance of other factors in explaining individual variation in imitative ability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".