The self-controlled motor learning literature consists of experiments that compare a group of learners who are provided with a choice over an aspect of their practice environment to a group who are yoked to those choices. A qualitative review of the literature suggests an unambiguous benefit from self-controlled practice. A meta-analysis was conducted on the effects of self-controlled practice on retention test performance measures with a focus on assessing and potentially correcting for selection bias in the literature, such as publication bias and p-hacking. First, a naïve random effects model was fit to the data and a moderate benefit of self-controlled practice, g = .44 (k = 52, N = 2061, 95% CI [.31, .56]), was found. Second, publication status was added to the model as a potential moderator, revealing a significant difference between published and unpublished findings, with only the former reporting a benefit of self-controlled practice. Third, to investigate and adjust for the impact of selectively reporting statistically significant results, a weight-function model was fit to the data with a one-tailed p-value cutpoint of .025. The weight-function model revealed substantial selection bias and estimated the true average effect of self- controlled practice as g = .107 (95% CI [.047, .18]). P-curve analyses were conducted on the statistically significant results published in the literature and the outcome suggested a lack of evidential value. Fourth, a suite of sensitivity analyses were conducted to evaluate the robustness of these results, all of which converged on trivially small effect estimates. Overall, our results suggest the benefit of self-controlled practice on motor learning is small and not currently distinguishable from zero.

The self-controlled motor learning literature consists of experiments that compare a group of learners who are provided with a choice over an aspect of their practice environment to a group who are yoked to those choices. A qualitative review of the literature suggests an unambiguous benefit from self-controlled practice. A meta-analysis was conducted on the effects of self-controlled practice on retention test performance measures with a focus on assessing and potentially correcting for selection bias in the literature, such as publication bias and p-hacking. First, a naïve random effects model was fit to the data and a moderate benefit of self-controlled practice, g=.44 (k= 52,N= 3134,95%CI[.31, .56]), was found. Second, publication status was added to the model as a potential moderator, revealing a significant difference between published and unpublished findings, with only the former reporting a benefit of self-controlled practice. Third, to investigate and adjust for the impact of selectively reporting statistically significant results, a weight-function model was fit to the data with a one-tailed p-value cutpoint of .025. The weight-function model revealed substantial selection bias and estimated the true average effect of self-controlled practice as g=.107 (95%CI[.047, .18]). P-curve analyses were conducted on the statistically significant results published in the literature and the outcome suggested a lack of evidential value. Fourth, a suite of sensitivity analyses were conducted to evaluate the robustness of these results, all of which converged on trivially small effect estimates. Overall, our results suggest the benefit of self-controlled practice on motor learning is small and not currently distinguishable from zero.

A priori power analyses can ensure studies are unlikely to miss interesting effects. Recent metascience has suggested that kinesiology research may be underpowered and selectively reported. Here, we examined whether power analyses are being used to ensure informative studies in motor behavior. We reviewed every article published in three motor behavior journals between January 2019 and June 2021. Power analyses were reported in 13% of studies (k = 636) that tested a hypothesis. No study targeted the smallest effect size of interest. Most studies with a power analysis relied on estimates from previous experiments, pilot studies, or benchmarks to determine the effect size of interest. Studies without a power analysis reported support for their main hypothesis 85% of the time, while studies with a power analysis found support 76% of the time. The median sample sizes were n = 17.5 without a power analysis and n = 16 with a power analysis, suggesting the typical study design was underpowered for all but the largest plausible effect size. At present, power analyses are not being used to optimize the informativeness of motor behavior research. Adoption of this widely recommended practice may greatly enhance the credibility of the motor behavior literature.

Enhanced expectancies and autonomy-support through self-controlled practice conditions form the motivation pillar of OPTIMAL theory (Wulf & Lewthwaite, 2016). 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 are instead small, uncertain, and potentially null. We also 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.

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