ABSTRACT

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.

摘要

通过自我控制的实践条件增强期望和自主支持构成了 OPTIMAL 理论的动机支柱 [Wulf, G., & Lewthwaite, R. (2016)]。通过学习的内在动机和注意力优化表现：运动学习的最佳理论。心理公告与评论, 23(5), 1382–1414。https://doi.org/10.3758/s13423-015-0999-9]。这些练习变量对运动学习的影响最近在两个独立的荟萃分析中进行了评估。两项荟萃分析均发现，已发表的文献表明对运动学习有适度且显着的益处；然而，在两篇文献中都发现了报告偏倚的证据。尽管在自我控制的荟萃分析中报告了多个偏差校正估计值，但没有原则性的方法可以优先选择一个。在增强的预期荟萃分析中，修剪和填充技术未能纠正估计的效果。在这里，我们通过使用稳健的贝叶斯荟萃分析方法重新分析来自两个荟萃分析的数据来解决这些限制。我们的再分析表明，报告偏差大大夸大了原始荟萃分析中这些实践变量的好处。真正的影响似乎很小、不确定，而且可能为零。我们发现来自原始荟萃分析的所有研究的估计平均统计功效为 6%（95% 置信区间 [5%, 13%]）。这些结果提供了令人信服且趋同的证据，强烈表明现有文献不足以支持 OPTIMAL 理论的动机支柱。如果运动学习科学家想要提出基于证据的建议，我们的结果强调需要有足够动力的实验设计。我们发现来自原始荟萃分析的所有研究的估计平均统计功效为 6%（95% 置信区间 [5%, 13%]）。这些结果提供了令人信服且趋同的证据，强烈表明现有文献不足以支持 OPTIMAL 理论的动机支柱。如果运动学习科学家想要提出基于证据的建议，我们的结果强调需要有足够动力的实验设计。我们发现来自原始荟萃分析的所有研究的估计平均统计功效为 6%（95% 置信区间 [5%, 13%]）。这些结果提供了令人信服且趋同的证据，强烈表明现有文献不足以支持 OPTIMAL 理论的动机支柱。如果运动学习科学家想要提出基于证据的建议，我们的结果强调需要有足够动力的实验设计。