Auditory-motor rhythm synchronization in children with autism spectrum disorder
Bibliographic record
Abstract
• Auditory-motor rhythm synchronization was tested in ASD and TD children. • ASD and TD children performed similarly across all rhythm complexities. • ASD and TD children both performed worse with increasing complexity. • ASD and TD children both performed better with age. • This work helps better characterize auditory-motor interactions in ASD. Autism spectrum disorder (ASD) is characterized by difficulties in social and communication skills as well as atypical sensory perception and motor skills. Sensorimotor abilities such as auditory-motor integration are essential for social interaction and communication. The goal of this research was to investigate the development of auditory-motor rhythm synchronization for the first time in ASD versus typically-developing (TD) children. Participants were 31 boys with ASD and 23 TD boys that were matched in age and IQ. Participants were tested on an auditory-motor rhythm synchronization task in which they tapped in synchrony with rhythms of varying metrical complexity. Both children with ASD and TD performed similarly on this task and both groups performed better with age. This work demonstrates that non-verbal rhythm synchronization is intact in ASD over the course of childhood development. This research serves to better understand sensorimotor interactions in ASD and to better define sensory phenotypes in ASD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".