Asymmetries in Perceptual Adjustments to Non-Canonical Pronunciations
Bibliographic record
Abstract
This paper examines two plausible mechanisms supporting sound category adaptation: directional shifts towards the novel pronunciation or a general category relaxation of criteria. Focusing on asymmetries in adaptation to the voicing patterns of English coronal fricatives, we suggest that typology or synchronic experience affect adaptation. A corpus study of coronal fricative substitution patterns confirmed that North American English listeners are more likely to be exposed to devoiced /z/ than voiced /s/. Across two perceptual adaptation experiments, listeners in test conditions heard naturally produced devoiced /z/ or voiced /s/ in critical items within sentences, while control listeners were exposed to identical sentences with canonical pronunciations. Perceptual adaptation was tested via a lexical decision test, with devoiced /z/ or voiced /s/, as well as a novel alveopalatalized pronunciation, to determine whether adaptation was targeted in the direction of the exposed variant or reflected a more general relaxation. Results indicate there was directional and word-specific adaptation for /z/-devoicing with no evidence for generalization. Conversely, there was evidence of /s/-voicing generalizing and eliciting general category relaxation. These results underscore the role of perceptual experiences, and support an evaluation stage in perceptual learning, where listeners assess whether to update a representation.
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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 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".