Department of Kinesiology, Michigan State University, 308 W Circle Dr, East Lansing, MI, 48824, USA.
Exp Brain Res. 2021 Jan;239(1):1-19. doi: 10.1007/s00221-020-05908-6. Epub 2020 Nov 10.
Motor learning encompasses a broad set of phenomena that requires a diverse set of experimental paradigms. However, excessive variation in tasks across studies creates fragmentation that can adversely affect the collective advancement of knowledge. Here, we show that motor learning studies tend toward extreme fragmentation in the choice of tasks, with almost no overlap between task paradigms across studies. We argue that this extreme level of task fragmentation poses serious theoretical and methodological barriers to advancing the field. To address these barriers, we propose the need for developing common 'model' task paradigms which could be widely used across labs. Combined with the open sharing of methods and data, we suggest that these model task paradigms could be an important step in increasing the robustness of the motor learning literature and facilitate the cumulative process of science.
运动学习涵盖了广泛的现象，需要多样化的实验范式。然而，研究中任务的过度变化会造成碎片化，从而对知识的整体发展产生不利影响。在这里，我们表明，运动学习研究在任务选择方面存在极端的碎片化趋势，几乎没有研究之间的任务范式重叠。我们认为，这种极端的任务碎片化程度对该领域的发展构成了严重的理论和方法障碍。为了解决这些障碍，我们提出需要开发通用的“模型”任务范式，可以在实验室中广泛使用。结合方法和数据的公开共享，我们认为这些模型任务范式是提高运动学习文献稳健性和促进科学积累过程的重要步骤。
