Predicting Explorative Motor Learning Using Decision-Making and Motor Noise

Until recently, motor learning was viewed as an automatic process that was independent, and even in conflict with higher-level cognitive processes such as decision-making. However, it is now thought that decision-making forms an integral part of motor learning. To further examine the relationship between decision-making and motor learning, we asked whether explorative motor learning could be considered a decision-making task that was adjusted for motor noise. We studied human performance in an explorative motor learning task and a decision-making task which involved a similar underlying structure with exception that it was not subject to motor
(execution) noise. In addition, we independently measured each participant’s level of motor noise. Crucially, with a computational model, we were able to predict participant explorative motor learning by using parameters estimated from the decision-making task and the separate motor noise task. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and reinforces the view that the mechanisms which control decision-making and motor behaviour are highly integrated.

直至近期，运动学习被视为一种独立且甚至与高级认知过程如决策制定相冲突的自动过程。然而，目前认为决策制定构成了运动学习的一个有机部分。为进一步探究决策制定与运动学习之间的关系，我们提出探究性运动学习是否可以被视为一种调整了运动噪声的决策任务。我们研究了人类在探究性运动学习任务以及一个涉及相似潜在结构但不受运动（执行）噪声影响的决策任务中的表现。此外，我们独立测量了每位参与者的运动噪声水平。关键在于，借助一个计算模型，我们能够利用从决策任务和独立的运动噪声任务中估计的参数来预测参与者的探究性运动学习。这表明探究性运动学习可以形式化为一种调整运动噪声的序列决策过程，并进一步强化了决策制定与运动行为控制机制高度整合的观点。