Inconvenient findings for the "optimal" theory of motor learning
Bibliographic record
Abstract
The OPTIMAL theory of motor learning (Wulf & Lewthwaite, 2016) was recently proposed to account for, from a motivational perspective, a select set of practice conditions that enhance learning. One of which was self-controlled practice wherein exercising choice over a practice feature produces a reliable learning advantage compared to being denied this choice (i.e., yoked practice). Within the OPTIMAL theory, Wulf and Lewthwaite argued that self-controlled groups are autonomy-supportive because exercising choice satisfies our basic psychological need for autonomy, which in turn enhances information-processing, perceptions of competency, self-efficacy, and sense of agency (pp. 1393-1394). Here, we argue that the OPTIMAL theory is a sub-optimal explanation for self-controlled learning advantages because it cannot explain all the data, many predictions are not clearly testable, and numerous predictions are not supported by subsequent data. For instance, there is little to no evidence supporting their claim that self-controlled groups are more autonomy-supportive than yoked groups (e.g., Ste-Marie et al., 2013). We therefore question the OPTIMAL theory as a viable explanation for self-controlled learning advantages. Instead, we argue that self-controlled learning advantages arise from more effective information-processing activities associated with performance-dependent strategies (e.g., error estimation) that ultimately reduce uncertainties regarding task performance (e.g., Carter et al., 2014; Grand et al., 2015). While we contend this perspective better accounts for existing data and that key predictions are supported, it too fails to fully capture self-controlled learning advantages. We outline current issues with both explanations and propose future avenues for this area of research.Acknowledgments: Supported by NSERC
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".