Department of Kinesiology, Michigan State University, East Lansing, MI, USA; Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA.
Department of Kinesiology, Michigan State University, East Lansing, MI, USA.
Neurosci Biobehav Rev. 2022 Oct;141:104852. doi: 10.1016/j.neubiorev.2022.104852. Epub 2022 Sep 1.
The ability to predict individual differences in motor learning has significant implications from both theoretical and applied perspectives. However, there is high variability in the methodological and analytical strategies employed as evidence for such predictions. Here, we critically examine the evidence for predictions of individual differences in motor learning by reviewing the literature from a 20-year period (2000-2020). Specifically, we examined four factors: (i) the predictor and predicted variables used, (ii) the strength of the prediction and associated sample size, (iii) the timescale over which the prediction was made, and (iv) the type of motor task used. Overall, the results highlight several issues that raise concerns about the quality of the evidence for such predictions. First, there was a large variation in both predictor and predicted variables, suggesting the presence of a large number of researcher degrees of freedom. Second, sample sizes tended to be small, and the strength of the correlation showed an inverse relation with sample size. Third, the timescale of most predictions was very short, mostly constrained to a single day. Last, most studies were largely restricted to two experimental paradigms - adaptation and sequence learning. Based on these issues, we highlight recommendations for future studies to improve the quality of evidence for predicting individual differences in motor learning.
预测个体在运动学习中的差异具有重要的理论和应用意义。然而，在用于支持此类预测的方法学和分析策略方面存在高度的可变性。在这里，我们通过回顾 20 年（2000-2020 年）的文献，从理论上仔细审查了预测个体在运动学习中的差异的证据。具体来说，我们检查了四个因素：（i）使用的预测因子和预测变量，（ii）预测的强度和相关样本大小，（iii）进行预测的时间尺度，以及（iv）使用的运动任务类型。总体而言，结果强调了几个问题，这些问题引起了对这种预测的证据质量的关注。首先，预测因子和预测变量都存在很大的差异，这表明存在大量的研究人员自由度。其次，样本量往往较小，相关性的强度与样本量呈反比。第三，大多数预测的时间尺度非常短，主要限于一天。最后，大多数研究主要局限于两种实验范式——适应和序列学习。基于这些问题，我们强调了对未来研究的建议，以提高预测个体在运动学习中的差异的证据质量。
