The relationship between perceived competence and intrinsic motivation and motor skill retention: An exploratory analysis
Bibliographic record
Abstract
In OPTIMAL theory it is argued that autonomy-supportive practice conditions improve motor performance and learning through increased perceptions of competence and intrinsic motivation (Wulf & Lewthwaite, 2016). Recently it has been found that these effects are not always consistent on the group level wherein increased perceptions of autonomy do not coincide with improved motor learning on the group level. There has instead been growing interest in these effects at an individual participant level. These findings, however, have been mixed, thus further investigation is required to better understand the role of an individual’s psychological constructs on motor learning. To this end, an exploratory secondary analysis was conducted on two experiments where increased perceptions of autonomy did not translate to improved retention on a group level. To investigate the predictions forwarded in OPTIMAL theory, we examined performance on retention of a cup stacking task in two experiments as a function of perceptions of competence and intrinsic motivation at the participant level. Motor learning was not enhanced in participants who reported higher perceived competence or intrinsic motivation as there was no relationship between either psychological construct and stacking time at retention. Sophisticated statistical techniques may be required to better understand whether there is a causal relationship between psychological constructs and motor learning. Currently, these findings add to the growing body of evidence that autonomy-support, perceived competence, and intrinsic motivation may not have a direct influence on motor learning as predicted in OPTIMAL theory.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".