Faster maturation of selective attention in musically trained children and adolescents: Converging behavioral and event‐related potential evidence
Bibliographic record
Abstract
Previous work suggests that musical training in childhood is associated with enhanced executive functions. However, it is unknown whether this advantage extends to selective attention-another central aspect of executive control. We recorded a well-established event-related potential (ERP) marker of distraction, the P3a, during an audio-visual task to investigate the maturation of selective attention in musically trained children and adolescents aged 10-17 years and a control group of untrained peers. The task required categorization of visual stimuli, while a sequence of standard sounds and distracting novel sounds were presented in the background. The music group outperformed the control group in the categorization task and the younger children in the music group showed a smaller P3a to the distracting novel sounds than their peers in the control group. Also, a negative response elicited by the novel sounds in the N1/MMN time range (~150-200 ms) was smaller in the music group. These results indicate that the music group was less easily distracted by the task-irrelevant sound stimulation and gated the neural processing of the novel sounds more efficiently than the control group. Furthermore, we replicated our previous finding that, relative to the control group, the musically trained children and adolescents performed faster in standardized tests for inhibition and set shifting. These results provide novel converging behavioral and electrophysiological evidence from a cross-modal paradigm for accelerated maturation of selective attention in musically trained children and adolescents and corroborate the association between musical training and enhanced inhibition and set shifting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".