Individual differences in cue weights are correlated across contrasts
Bibliographic record
Abstract
When listeners make judgments about phonological contrasts, they integrate different acoustic dimensions putting more weight on some than others. Individuals differ in how they weight different cues. Schultz, Francis & Llanos (2012) found that weights for VOT and f0 as cues to initial stop voicing in English are weakly positively correlated across individuals. We ask whether this is specific to VOT and f0 or a more general property of cue weighting across individuals. Secondly, across contrasts do the same listeners have stronger cue weights? 43 listeners performed a 2AFC task for four sets of minimal pairs that each varied orthogonally in two dimensions. All heard bet-bat and Luce-lose (vowel spectral quality vs. duration) and bog-dog (burst spectrum vs. formant transitions). 24 participants also heard sock-shock (sibilant spectrum vs. formant transitions) and the other 19 heard dear-tear (VOT vs. f0). Cue weights were fit with random slopes in a logistic regression for each minimal pair. Weights were positively correlated across individuals both within and across contrasts for bet-bat, Luce-lose, and sock-shock. Bog-dog and dear-tear had less consistent results. Overall this indicates that some individuals are better able to extract and use acoustic-phonetic information across different acoustic dimensions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".