Manipulating the characteristics of self-controlled feedback schedules
Bibliographic record
Abstract
The OPTIMAL theory of motor learning suggests that self-controlled practice conditions—in which participants are asked to make task-relevant (e.g., feedback) or task-irrelevant (e.g., ball colour) choices during practice—enhance learning over no-choice yoked conditions because they support the learner's need for autonomy. Based on OPTIMAL theory, we predicted that participants in self-controlled groups would show enhanced learning relative to their yoked counterparts regardless of the characteristics of their feedback. One self-controlled group exercised choice over an error feedback schedule (e.g., -186 ms) and the other had choice over a graded feedback schedule (e.g., too fast). Corresponding yoked groups were collected. All participants (N=152) practiced an aiming task requiring a rapid 40-degrees extension movement in exactly 225 ms. Measures of intrinsic motivation, perceived competence, and perceived autonomy were collected before, during, and at the end of practice, and before the 24-hr retention and transfer tests. For the spatial and temporal goals, participants performed more accurately in the retention test than in the transfer test. We found a spatial learning advantage for graded feedback. Contrary to previous findings, we did not find a self-controlled learning advantage. Self-reported intrinsic motivation increased over time and participants who received error feedback reported higher perceived competence than those who received graded feedback. Consistent with previous findings, perceived autonomy was similar across groups. These data are not in line with tenets of the OPTIMAL theory and add to the growing evidence that self-controlled conditions should not be labelled as autonomy-supportive.Acknowledgments: Supported by NSERC
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".