Influence of Musical Expertise on Segmental and Tonal Processing in Mandarin Chinese
Bibliographic record
Abstract
A same-different task was used to test the hypothesis that musical expertise improves the discrimination of tonal and segmental (consonant, vowel) variations in a tone language, Mandarin Chinese. Two four-word sequences (prime and target) were presented to French musicians and nonmusicians unfamiliar with Mandarin, and event-related brain potentials were recorded. Musicians detected both tonal and segmental variations more accurately than nonmusicians. Moreover, tonal variations were associated with higher error rate than segmental variations and elicited an increased N2/N3 component that developed 100 msec earlier in musicians than in nonmusicians. Finally, musicians also showed enhanced P3b components to both tonal and segmental variations. These results clearly show that musical expertise influenced the perceptual processing as well as the categorization of linguistic contrasts in a foreign language. They show positive music-to-language transfer effects and open new perspectives for the learning of tone languages.
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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 | 0.002 |
| 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) | 0.001 | 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".