Imagined movement accuracy is strongly associated with drivers of overt movement error and weakly associated with imagery vividness.
Bibliographic record
Abstract
Theories of motor imagery conflict in their account of what happens during an imagined movement, with some suggesting that movement is simulated while others suggest it involves creating and elaborating upon an internal representation of the movement. Here we report evidence that imagery involves the simulation of a movement and that it varies in accuracy. Two groups of participants performed a motor task focused on challenging movement execution either overtly or via motor imagery. Overt performance was used to model expected performance given required movement characteristics (i.e., speed, complexity, familiarity), which was then compared with self-reported accuracy during imagery. Movement characteristics had a large effect on self-reported accuracy compared with a small effect of imagery vividness. Self-reported accuracy improved across trials with familiar movements compared with novel movements in a similar manner for each group. The complexity of the imagined movement did not influence movement time during imagery or overt trials, further suggesting that imagined movements are simulated rather than abstractly represented. Our results therefore support models of motor imagery that involve the simulation of a movement and its viability, which may be the basis of imagery-based motor learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.041 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".