A comparison of constrained and unconstrained reaching movements by people with and without autism spectrum disorders
Bibliographic record
Abstract
Although not included in the definition of Autism spectrum disorder (ASD), motor performance differences are reported consistently. Specifically, individuals with ASD spend more time preparing movements. Currently there is a debate as to whether people with ASD require more time to execute reaching movements. The present study compared constrained and unconstrained reaching movements in order to determine if previous reports of a preference for proprioceptive feedback explains inconsistent movement time reports in the literature. Eleven ASD participants and 13 typically developing (TD) participants were recruited. All participants performed three types of reaching movements in the sagittal plane: 1) sliding along a track on a piece of Plexiglas (1D, constrained); 2) sliding along a smooth piece of Plexiglas (2D, constrained); and 3) aiming movements (3D, unconstrained). Movements were recorded using a 3D motion analysis system (3D Investigator, NDI) and muscle activity was recorded using surface electromyography (CED 1902 dual system amplifier). All dependent variables were submitted to a 2 Group (ASD, TD) by 3 Movement Type (1D, 2D, 3D) mixed analysis of variance. Results revealed a significant Group by Movement Type interaction for premotor reaction time, movement time, and time after peak velocity. The ASD group took longer to prepare and execute 3D (unconstrained) movements compared to the TD group. The current findings are consistent with previously reported behavioral and brain imaging research identifying altered brain connections in ASD participants indicative of a preference for haptic over visual feedback for movement control.Acknowledgments: Funding: Research Manitoba; The present research was also made possible by funding from the Canadian Foundation for Innovation. The authors would like to thank Kelsey Brown and Brie Page for their help collecting data and all of the participants for their time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".