Abstract
It is well-established that people can factor into account the distribution of their errors in motor performance so as to optimize reward. Here we asked whether, in the context of motor learning where errors decrease across trials, people take into account their future, improved performance so as to make optimal decisions to maximize reward. One group of participants performed a virtual throwing task in which, periodically, they were given the opportunity to select from a set of smaller targets of increasing value. A second group of participants performed a reaching task under a visuomotor rotation in which, after performing a initial set of trials, they selected a reward structure (ratio of points for target hits and misses) for different exploitation horizons (i.e., numbers of trials they might be asked to perform). Because movement errors decreased exponentially across trials in both learning tasks, optimal target selection (task 1) and optimal reward structure selection (task 2) required taking into account future performance. The results from both tasks indicate that people anticipate their future motor performance so as to make decisions that will improve their expected future reward.
Highlights
An inherent component of sensorimotor control involves dealing with movement variability that arises from sensory and motor noise [1,2,3]
A hallmark of motor learning is the reduction of performance errors with practice, which can have important ramifications for decision making
In this paper we asked whether, in the context of two different motor learning tasks, people take into account their future, improved performance so as to make optimal decisions to maximize reward
Summary
An inherent component of sensorimotor control involves dealing with movement variability that arises from sensory and motor noise [1,2,3]. In a task developed by Trommershauser and colleagues [14], participants made rapid pointing movements towards partially overlapping reward and penalty regions (circles) presented on a computer touchscreen These studies showed that participants selected an aim location (which could be far from the center of the reward region) that was near-optimal in terms of maximizing reward, indicating that they accurately incorporated their own movement variability in motor planning [14,15]. A subsequent experiment showed that when presented with two configurations, each consisting of a reward region and a partially overlapping penalty region, participants rapidly selected, and optimally aimed towards, the configuration that had the highest expected gain given their movement variability [16] These results suggest that people can rapidly take into account their movement variability when making strategic decisions about where to reach. When movement variability was artificially increased via altered visual feedback, participants accurately updated their estimate of this variability so as to achieve near-optimal performance [17,18,19]
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