Perceptual learning of plain-ejective contrast by naïve listeners
Bibliographic record
Abstract
A recent cross-linguistic study finds that the presence of post-burst silence versus aspiration is a primary cue for ejective-plain stop contrast while VOT (voice onset time) plays a minor role (Percival, 2024).This study explores whether English listeners can acquire this release cue after a short exposure and distributional training: "natural correlation" or "inhibition" (Kondaurova and Francis, 2010).Target words were created from Q'anjob'al /tu/ and /t'u/, distinguished by both VOT (short versus high) and release (aspiration versus silence).In the exposure phase, listeners heard stimuli along with pseudoorthographic forms, or .In the training phase, listeners were given feedback: the "natural correlation" group (n=31) was trained on the same stimuli as the exposure phase; for the "inhibition" group (n=29), only the release cue distinguished the stops and VOT varied from short to long for both stops.The pre-test and post-test show that the release cue, already a primary cue prior to training for most listeners, especially with longer VOT, became stronger after training.No group difference was found.The novel release cue likely makes ejectives poor exemplars of English stops, therefore, easy to distinguish from plain stops (Best et al. 2001).
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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 | 0.003 |
| 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) | 0.003 | 0.001 |
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".