Development and Validation of the Combined Action Observation and Motor Imagery Ability Questionnaire
Bibliographic record
Abstract
Combined use of action observation and motor imagery (AOMI) is an increasingly popular motor-simulation intervention, which involves observing movements on video while simultaneously imagining the feeling of movement execution. Measuring and reporting participant imagery-ability characteristics are essential in motor-simulation research, but no measure of AOMI ability currently exists. Accordingly, the AOMI Ability Questionnaire (AOMI-AQ) was developed to address this gap in the literature. In Study 1, two hundred eleven participants completed the AOMI-AQ and the kinesthetic imagery subscales of the Movement Imagery Questionnaire-3 and Vividness of Motor Imagery Questionnaire-2. Following exploratory factor analysis, an 8-item AOMI-AQ was found to correlate positively with existing motor-imagery measures. In Study 2, one hundred seventy-four participants completed the AOMI-AQ for a second time after a period of 7-10 days. Results indicate a good test-retest reliability for the AOMI-AQ. The new AOMI-AQ measure provides a valid and reliable tool for researchers and practitioners wishing to assess AOMI ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".