Abstract
A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.
Highlights
Human motor learning has mainly been examined through motor adaptation tasks in which participants are exposed to a novel perturbation during reaching movements [1,2,3,4]
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
The main purpose of this study was to test whether explorative motor learning and decisionmaking could both be understood as a sequential decision process that optimises reward given task, sensory and/or motor uncertainty
Summary
Human motor learning has mainly been examined through motor adaptation tasks in which participants are exposed to a novel perturbation during reaching movements [1,2,3,4]. Despite being significantly slower and more variable than learning through a sensory prediction error, recent work has shown that participants are able to identify and adjust specific features of a movement, such as the curvature of a trajectory, through a reinforcement signal [8, 11,12,13,14,15]. Such explorative motor learning has been explained using reinforcement models in which learning is driven by a reward prediction error. This enables actions to be selected based on the probability of yielding future rewards [11, 13, 15, 16]
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