Please don’t stop the music: A meta-analysis of the cognitive and academic benefits of instrumental musical training in childhood and adolescence
Bibliographic record
Abstract
An extensive literature has investigated the impact of musical training on cognition and academic achievement in children and adolescents. However, most of the studies have relied on cross-sectional designs, which makes it impossible to elucidate whether the observed differences are a consequence of the engagement in musical activities. Previous meta-analyses with longitudinal studies have also found inconsistent results, possibly due to their reliance on vague definitions of musical training. In addition, more evidence has appeared in recent years. The current meta-analysis investigates the impact of early programs that involve learning to play musical instruments on cognitive and academic skills, as previous meta-analyses have not focused on this form of musical training. Following a systematic search, 34 independent samples of children and adolescents were included. All the studies had pre-post designs and, at least, one control group. Overall, we found a small but significant benefit (g = 0.24) with short-term programs, regardless of whether they were randomized or not. In addition, a small advantage at baseline was observed in studies with self-selection (g = 0.28), indicating that participants who had the opportunity to select the activity consistently showed a slightly superior performance prior to the beginning of the intervention. Our findings support a nature and nurture approach to the relationship between instrumental training and cognitive performance. Nevertheless, evidence from well-conducted studies is still scarce and more studies are necessary to reach firmer conclusions.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".