Perceptual effects of lexical competition on Cantonese tone categories
Bibliographic record
Abstract
Listeners use lexical information and the speech signal to categorize sounds and recognize words despite substantial acoustic-phonetic variation in natural speech. In diachronic mergers, where systematic variation acts to neutralize lexical contrasts, the role of the lexicon becomes less clear. We examined how lexical competition structures phonetic variability of (merging) lexical tone categories in Cantonese using three experiments. Listeners categorized tokens from lexical tone continua generated from minimal pairs (Experiment 1: Word identification) and categorized tokens from tone continua generated from word-nonword pairs (Experiment 2: Lexical decision). The presence of a lexical competitor at both continuum endpoints in Experiment 1 maintained more discrete categorization functions for non-merging tone pairs than in Experiment 2 where only one endpoint was a word. In the merging tone pairs, categorization was less discrete and the effect of lexical competition was different. Exploratory data from a goodness rating task, Experiment 3, suggest that lexical competition affects internal category structure for merging tones, but not non-merging tones. Overall, these data provide evidence that tone mergers affect phonetic category boundaries and internal category structure in the lexicon, and that, for non-merging tones, the range of acceptable phonetic variation is constrained by the presence of a lexical competitor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".