Effects of musical and linguistic experience on categorization of lexical and melodic tones
Bibliographic record
Abstract
This study investigated the categorization of Mandarin lexical tones and music melodic tones by listeners differing in linguistic and musical experience (English musicians, English non-musicians, and Mandarin non-musicians). Linguistic tonal continua were created from the Mandarin rising to level, and falling to level tones. Melodic continua were created by varying the note D under the context of C and E. The tasks involved tone discrimination and identification. Results revealed that musical training facilitated Mandarin tone categorization, with English musicians' tone identification approximating native Mandarin patterns, being more categorical than English non-musicians'. However, English musicians showed higher discrimination accuracy than Mandarin listeners but not English non-musicians. This suggests that musical experience was not advantageous in discriminating linguistic tonal variations, which requires listeners to ignore subtle physical differences in order to make categorical judgments. Similarly, Mandarin tone experience affected melodic tone identification, with Mandarin non-musicians approximating English musicians, showing more categorical patterns than English non-musicians. In contrast, Mandarin non-musicians' melodic discrimination was the poorest among the three groups, indicating that their experience with linguistic tone categorization may have decreased their sensitivity to fine-grained pitch variations. These results demonstrate bi-directional transfer of pitch proficiency between speech and music as a function of experience.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".