Individual differences in musical ability are stable over time in childhood
Bibliographic record
Abstract
The development of human abilities stems from a complex interplay between genetic predispositions and environmental factors. Numerous studies have compared musicians with non-musicians on measures of musical and non-musical ability, frequently attributing musicians' superior performance to their training. By ignoring preexisting differences, however, this view assumes that taking music lessons is akin to random assignment. In the present longitudinal study, the musical ability of 5- to 10-year-olds was measured at Time 1 with a test of music perception and cognition. Five years later, at Time 2, the children took the same test and a second test designed for older listeners. The test-retest correlation for aggregate scores was remarkably high, r ≈ 0.7, and remained strong when confounding variables (age, cognitive abilities, personality) were held constant. At both time points, music training was associated with musical ability, but the association at Time 2 became nonsignificant when musical ability at Time 1 was held constant. Time 1 musical ability also predicted duration of subsequent music training. These data are consistent with results from genetic studies, which implicate genes in all aspects of musical behavior and achievement, and with meta-analyses, which indicate that transfer effects from music training are weak. In short, early musical abilities significantly predicted later abilities, demonstrating that individual differences are stable over time. We found no evidence, however, to suggest that music training predicted musical ability after accounting for prior ability. The results underscore the importance of considering preexisting abilities in any type of learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".