Enhanced expectancies and autonomy-support through self-controlled practice conditions form the motivation pillar of OPTIMAL theory [Wulf, G., & Lewthwaite, R. (2016). Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning. Psychonomic Bulletin & Review, 23(5), 1382–1414. https://doi.org/10.3758/s13423-015-0999-9]. The influence of these practice variables on motor learning was recently evaluated in two separate meta-analyses. Both meta-analyses found that the published literature suggested a moderate and significant benefit on motor learning; however, evidence for reporting bias was found in both literatures. Although multiple bias-corrected estimates were reported in the self-controlled meta-analysis, there was no principled way to prefer one over the other. In the enhanced expectancies meta-analysis, the trim-and-fill-technique failed to correct the estimated effects. Here, we addressed these limitations by reanalyzing the data from both meta-analyses using robust Bayesian meta-analysis methods. Our reanalysis revealed that reporting bias substantially exaggerated the benefits of these practice variables in the original meta-analyses. The true effects appear small, uncertain, and potentially null. We found the estimated average statistical power among all studies from the original meta-analyses was 6% (95% confidence interval [5%, 13%]). These results provide compelling and converging evidence that strongly suggests the available literature is insufficient to support the motivation pillar of OPTIMAL theory. Our results highlight the need for adequately powered experimental designs if motor learning scientists want to make evidence-based recommendations.

The present study investigated whether motivation and augmented feedback processing explain the effect of an incidental choice on motor learning, and examined whether motivation and feedback processing generally predict learning. Accordingly, participants were assigned to one of two groups, choice or yoked, then asked to practice a nondominant arm beanbag toss. The choice group was allowed to choose the color of the beanbag with which they made the toss, whereas the yoked group was not. Motor learning was determined by delayed-posttest accuracy and precision. Motivation and augmented feedback processing were indexed via the Intrinsic Motivation Inventory and electroencephalography, respectively. We predicted the choice group would exhibit greater motor learning, motivation, and augmented feedback processing, and that the latter two variables would predict learning. Results showed that an incidental choice failed to enhance motor learning, motivation, or augmented feedback processing. In addition, neither motivation nor augmented feedback processing predicted motor learning. However, motivation and augmented feedback processing were correlated, with both factors predicting changes in practice performance. Thus, results suggest the effect of incidental choices on motor learning may be tenuous, and indicate motivation and augmented feedback processing may be more closely linked to changes in practice performance than motor learning.

A multidisciplinary journal on gaming and gamification for health education/promotion, teaching and social change.