Effects of Background Music on Attentional Networks of Children With and Without Attention Deficit/Hyperactivity Disorder: Case Control Experimental Study
Bibliographic record
Abstract
Background To sustain performance during a task that requires attention may be a challenge for children with attention deficit/hyperactivity disorder (ADHD), which strongly influences motivation for tasks and has been connected to the level of arousal. Objective This study aimed to analyze the effect of musical stimulus on attentional performance in children with ADHD and typically developing children. Methods A total of 76 boys (34 with ADHD and 42 typically developing) performed the Attention Network Test (ANT) for children under 2 experimental conditions (with and without music). Four attentional measures were extracted from the ANT. We tested the effect of the experimental condition and its interaction with the group using repeated measures ANOVA. Results We found no significant main effects or interactions for the reaction times of the alerting, orienting, and conflict attentional networks of the ANT (all P>.05). Regarding ANT errors, we found a significant main effect for music, with a moderate effect size (F1,72=9.83; P=.03; ηp2=0.06) but the condition×group interaction was not significant (F1,72=1.79; P=.18). Participants made fewer errors when listening to music compared to the control condition. Conclusions Music seems not to interfere in the attentional network in children and adolescents. Perhaps background music affects motivation. Future studies will be needed to validate this. Trial Registration ReBEC.gov U1111-12589039; https://ensaiosclinicos.gov.br/rg/RBR-8s22sh8
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".