Can an iPad task determine visuomotor deficits in children with ASD?
Bibliographic record
Abstract
Autism Spectrum Disorders (ASD) are clinically defined through subjective measures. However, there is conclusive evidence of quantifiable motor deficits characterized in ASD. The objective of this study was to determine if a visuomotor memory task could be used to quantify differences between typically developing children and those with ASD. Children clinically diagnosed with ASD and no other comorbid intellectual disabilities (N=9) between the ages of 6 and 13 and typically developing children (N=17) between the ages of 5 and 8 participated in this study. All children performed a visuomotor memory task that was administered through a custom-made iPad application. The task began when the participants placed their index finger on the "home" position at the bottom of the screen. Following a random fore period, a single target appeared at a random location on the black screen. The participants were asked to tap the target (a 1cm diameter yellow dot) as quickly and accurately as possible, however the target would disappear as soon as the participant lifted his or her finger off the "home" position. An independent samples t-test was conducted to compare tapping accuracy in both populations as measured by distance (in pixels) from the center of the target. Although not significant, children with ASD were less accurate (M=160.55, SD=168.17) than typically developing children (M=39.27, SD=18.27); t(11.4)=-2.03, p0.081. A Pearson correlation revealed that there was a negative correlation between the attention to detail component of the Autism Quotient questionnaire (AQ) and accuracy score in children with ASD (r= -0.7, n=8, p=0.026). These findings provide encouragement that a visuomotor memory task on an iPad can be used to detect motor planning deficits in children with ASD as well as provide an objective measure of severity within one component of the AQ. Meeting abstract presented at VSS 2013
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".