Rethinking the role of music in the neurodevelopment of autism spectrum disorder
Bibliographic record
Abstract
Music has played a prominent role in the clinical and research literature on autism spectrum disorder (ASD) in regard to diagnosis, therapy, and behavioral observations of exceptional artistic abilities in this population. Music as therapy for ASD has traditionally focused on social interaction, communication skills, and social-emotional behaviors. However, recently, there has been an increased research focus on the role of motor and attention functions as part of the hallmark features of ASD, which may have significant implications for the role of music as an intervention for individuals with autism. The purpose of this article is to provide a critical appraisal of new research developments for therapists and researchers to potentially reassess the role of music as intervention to support healthy neurodevelopment in individuals with ASD and expand the current clinical scope of practice in music therapy for autism. Our argument is based upon compelling research evidence indicating that motor and attention deficits are deeply implicated in the healthy neurodevelopment of socio-communication skills and may be key indicators of structural and functional brain dysfunction in ASD. In light of this evidence, we suggest that music-based developmental training for attention and motor control may receive a critical new functional role in the treatment of autism due to the significant effect of auditory-motor entrainment on motor and attention functions and brain connectivity.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".