Examining the cognitive and perceptual perspectives of music-to-language transfer: A study of Cantonese-English bilingual children
Bibliographic record
Abstract
Motivated by theories of music-to-language transfer, we investigated whether and how musicianship benefits phonological and lexical prosodic awareness in first language (L1) Cantonese and second language (L2) English. We assessed 86 Cantonese-English bilingual children on rhythmic sensitivity, pitch sensitivity, non-verbal intelligence, inhibitory control, working memory, Cantonese phonological awareness, Cantonese tone awareness, English phonological awareness, and English stress awareness. Based on their prior music learning experience, we classified the children as musicians and non-musicians. The musicians performed better than the non-musicians on Cantonese phonological awareness, Cantonese tone awareness, and English phonological awareness. Additionally, the musicians had superior pitch sensitivity, non-verbal intelligence, inhibitory control, and working memory than the non-musicians. For Cantonese and English phonological awareness, neither cognitive abilities nor pitch and rhythmic sensitivities turned out to be unique predictors. However, working memory uniquely predicted Cantonese tone awareness, with age, rhythmic sensitivity, and pitch sensitivity controlled. From a theoretical perspective, our findings on Cantonese tone awareness favors the cognitive perspective of music-to-language transfer, in which working memory enhancement could explain the musicians’ superior performance in Cantonese tone awareness. However, our findings on phonological awareness do not favor the cognitive perspective, nor do they favor the perceptual perspective, in which enhanced rhythmic and pitch sensitivities could explain musicians’ advantage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".