Individual uniformity in phonetic imitation: Assessing the stability of individual variability across features and tasks
Bibliographic record
Abstract
• How consistent are individual patterns of phonetic imitation across features and tasks? • Tested explicit and implicit imitation of subphonemic VOT and F2 differences. • Cross-feature individual consistency found for explicit, but not implicit imitation. • Within-feature individual consistency found for both types of imitation. • Small but significant correlation in individual performance across tasks. Extensive individual variability has been reported in both spontaneous phonetic convergence and in explicit phonetic imitation tasks. This work tests the consistency of these individual patterns: are some individuals just globally more imitative, showing greater-than-average imitation regardless of the specific phone being imitated, and regardless of the type of imitation, or does an individual’s extent of imitation depend heavily on the phonetic content or the type of task? We examine the stability of individual variability in imitation of two types of subphonemic differences (VOT of voiceless stops and F2 of the vowels /æ/ and /u/), in two types of imitation tasks (implicit and explicit). We found that individuals' degree of imitation was significantly related across different phones within the same class (e.g., imitation of /p/ vs. /t/) in both implicit and explicit imitation tasks, but that individuals' degree of imitation of phones from different classes (e.g., imitation of stops vs. vowels) was only related in explicit, but not implicit, imitation. Findings are consistent with the idea that general cognitive or personality traits may govern individual variability in explicit imitation, but challenge the idea that they play any measurable role in predicting individual variability in implicit or spontaneous imitation. We also found a weak but significant correspondence between individual performance on the implicit and explicit imitation tasks, providing evidence that the two tasks rely on shared mechanisms, as well as a significant relationship between discrimination performance and explicit, but not implicit, imitation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".