Department of Experimental and Clinical Medicine, Physiological Sciences Section, University of Florence, Viale Morgagni 63, 50134, Florence, Italy.
School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.
Sci Rep. 2021 Mar 1;11(1):4829. doi: 10.1038/s41598-021-84369-5.
How strategies are formulated during a performance is an important aspect of motor control. Knowledge of the strategy employed in a task may help subjects achieve better performances, as it would help to evidence other possible strategies that could be used as well as help perfect a certain strategy. We sought to investigate how much of a performance is conditioned by the initial state and whether behavior throughout the performance is modified within a short timescale. In other words, we focus on the process of execution and not on the outcome. To this scope we used a repeated continuous circle tracing task. Performances were decomposed into different components (i.e., execution variables) whose combination is able to numerically determine movement outcome. By identifying execution variables of speed and duration, we created an execution space and a solution manifold (i.e., combinations of execution variables yielding zero discrepancy from the desired outcome) and divided the subjects according to their initial performance in that space into speed preference, duration preference, and no-preference groups. We demonstrated that specific strategies may be identified in a continuous task, and strategies remain relatively stable throughout the performance. Moreover, as performances remained stable, the initial location in the execution space can be used to determine the subject's strategy. Finally, contrary to other studies, we demonstrated that, in a continuous task, performances were associated with reduced exploration of the execution space.
在执行任务时，策略的制定是运动控制的一个重要方面。了解任务中使用的策略可以帮助受试者取得更好的表现，因为这有助于证明其他可能的策略，并帮助完善某种策略。我们试图研究有多少表现受到初始状态的影响，以及在短时间内整个表现是否会发生变化。换句话说，我们关注的是执行过程，而不是结果。为此，我们使用了重复的连续圆形追踪任务。表现被分解为不同的组成部分（即执行变量），它们的组合能够以数字方式确定运动的结果。通过确定速度和持续时间的执行变量，我们创建了一个执行空间和一个解决方案流形（即产生与期望结果零差异的执行变量组合），并根据受试者在该空间中的初始表现将其分为速度偏好组、持续时间偏好组和无偏好组。我们证明，在连续任务中可以识别特定的策略，并且策略在整个表现过程中相对稳定。此外，由于表现保持稳定，执行空间中的初始位置可用于确定受试者的策略。最后，与其他研究不同，我们证明在连续任务中，表现与执行空间的探索减少有关。
