A tale of too many tasks: task fragmentation in motor learning and a call for model task paradigms

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.