Does musicianship influence the perceptual integrality of tones and segmental information?
Bibliographic record
Abstract
This study investigated the effect of musicianship on the perceptual integrality of tones and segmental information in non-native speech perception. We tested 112 Cantonese musicians, Cantonese non-musicians, English musicians, and English non-musicians with a modified Thai tone AX discrimination task. In the tone discrimination task, the control block only contained tonal variations, whereas the orthogonal block contained both tonal and task-irrelevant segmental variations. Relative to their own performance in the control block, the Cantonese listeners showed decreased sensitivity index (d') and increased response time in the orthogonal block, reflecting integral perception of tones and segmental information. By contrast, the English listeners performed similarly across the two blocks, indicating independent perception. Bayesian analysis revealed that the Cantonese musicians and the Cantonese non-musicians perceived Thai tones and segmental information equally integrally. Moreover, the English musicians and the English non-musicians showed similar degrees of independent perception. Based on the above results, musicianship does not seem to influence tone-segmental perceptual integrality. While musicianship apparently enhances tone sensitivity, not all musical advantages are transferrable to the language domain.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".