Flexible Cognitive Strategies during Motor Learning

Visuomotor rotation tasks have proven to be a powerful tool to study adaptation of the motor system. While adaptation in such tasks is seemingly automatic and incremental, participants may gain knowledge of the perturbation and invoke a compensatory strategy. When provided with an explicit strategy to counteract a rotation, participants are initially very accurate, even without on-line feedback. Surprisingly, with further testing, the angle of their reaching movements drifts in the direction of the strategy, producing an increase in endpoint errors. This drift is attributed to the gradual adaptation of an internal model that operates independently from the strategy, even at the cost of task accuracy. Here we identify constraints that influence this process, allowing us to explore models of the interaction between strategic and implicit changes during visuomotor adaptation. When the adaptation phase was extended, participants eventually modified their strategy to offset the rise in endpoint errors. Moreover, when we removed visual markers that provided external landmarks to support a strategy, the degree of drift was sharply attenuated. These effects are accounted for by a setpoint state-space model in which a strategy is flexibly adjusted to offset performance errors arising from the implicit adaptation of an internal model. More generally, these results suggest that strategic processes may operate in many studies of visuomotor adaptation, with participants arriving at a synergy between a strategic plan and the effects of sensorimotor adaptation.Author Summary: Motor learning has been modeled as an implicit process in which an error, signaling the difference between the predicted and actual outcome is used to modify a model of the actor-environment interaction. This process is assumed to operate automatically and implicitly. However, people can employ cognitive strategies to improve performance. It has recently been shown that when implicit and explicit processes are put in opposition, the operation of motor learning mechanisms will offset the advantages conferred by a strategy and eventually, performance deteriorates. We present a computational model of the interplay of these processes. A key insight of the model is that implicit and explicit learning mechanisms operate on different error signals. Consistent with previous models of sensorimotor adaptation, implicit learning is driven by an error reflecting the difference between the predicted and actual feedback for that movement. In contrast, explicit learning is driven by an error based on the difference between the feedback and target location of the movement, a signal that directly reflects task performance. Empirically, we demonstrate constraints on these two error signals. Taken together, the modeling and empirical results suggest that the benefits of a cognitive strategy may lie hidden in many motor learning tasks.