Is auditory perceptual timing a core deficit of developmental coordination disorder?
Bibliographic record
Abstract
Time is an essential dimension for perceiving and processing auditory events, and for planning and producing motor behaviors. Developmental coordination disorder (DCD) is a neurodevelopmental disorder affecting 5-6% of children that is characterized by deficits in motor skills. Studies show that children with DCD have motor timing and sensorimotor timing deficits. We suggest that auditory perceptual timing deficits may also be core characteristics of DCD. This idea is consistent with evidence from several domains, (1) motor-related brain regions are often involved in auditory timing process; (2) DCD has high comorbidity with dyslexia and attention deficit hyperactivity, which are known to be associated with auditory timing deficits; (3) a few studies report deficits in auditory-motor timing among children with DCD; and (4) our preliminary behavioral and neuroimaging results show that children with DCD at age 6 and 7 have deficits in auditory time discrimination compared to typically developing children. We propose directions for investigating auditory perceptual timing processing in DCD that use various behavioral and neuroimaging approaches. From a clinical perspective, research findings can potentially benefit our understanding of the etiology of DCD, identify early biomarkers of DCD, and can be used to develop evidence-based interventions for DCD involving auditory-motor training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".