Contributions of age of start, cognitive abilities and practice to musical task performance in childhood
Bibliographic record
Abstract
Studies with adult musicians show that beginning lessons before age seven is associated with better performance on musical tasks and enhancement in auditory and motor brain regions. It is hypothesized that early training interacts with periods of heightened neural development to promote greater plasticity and better learning and performance later in life. However, we do not know whether such effects can be observed in childhood. Moreover, we do not know the degree to which such effects are related to training, or whether early training has different effects on particular musical skills depending on their cognitive, perceptual or motor requirements. To address these questions, we compared groups of child musicians who had started lessons earlier or later on age-normed tests of rhythm synchronization and melody discrimination. We also matched for age, years of experience, working memory and global cognitive ability. Results showed that children who started early performed better on simple melody discrimination and that scores on this task were predicted by both age of start (AoS) and cognitive ability. There was no effect of AoS for the more complex rhythm or transposed melody tasks, but these scores were significantly predicted by working memory ability, and for transposed melodies, by hours of weekly practice. These findings provide the first evidence that earlier AoS for music training in childhood results in enhancement of specific musical skills. Integrating these results with those for adult musicians, we hypothesize that early training has an immediate impact on simple melody discrimination skills that develop early, while more complex abilities, like synchronization and transposition require both further maturation and additional training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".