A mixed methods exploration of motor imagery in autistic and non-autistic adults: Diverse experiences and implications for interventions
Bibliographic record
Abstract
Research on motor imagery (MI) in non-autistic individuals suggests that there are shared neural circuitries between imagery and execution. The relationship between imagined and executed movements, and the use of MI in autistic adults is poorly understood. This study explored MI comprehension, prior use of MI, and subjective experiences during MI in autistic and non-autistic adults. Twenty autistic and twenty non-autistic individuals responded to a series of questions probing their understanding of and engagement in MI. Participants then completed the Kinesthetic and Visual Imagery Questionnaire (KVIQ), and reported on their subjective experiences during MI. Although there were no differences between the autistic and non-autistic individuals in their understanding of MI, the non-autistic group may have more prior use of MI in their everyday lives. Additionally, autistic participants generally reported less vivid imagery on the KVIQ compared to non-autistic participants, however experiences during MI varied widely across both groups ranging from vivid/intense images/sensations to the inability to imagine. In summary, some autistic individuals are able to engage in MI, but, similar to their non-autistic peers, MI ability and experiences vary across individuals. This work has important implications for MI interventions aimed at improving motor coordination.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".