Predicting Explorative Motor Learning Using Decision-Making and Motor Noise
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Description: 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.