Visuomotor adaptation has been thought to be an implicit process that results when a sensory-prediction error signal is used to update a forward model. A striking feature of human competence is the ability to receive verbal instructions and employ strategies to solve tasks; such explicit processes could be used during visuomotor adaptation. Here, we used a novel task design that allowed us to obtain continuous verbal reports of aiming direction while participants learned a visuomotor rotation. We had two main hypotheses: the contribution of explicit learning would be modulated by instruction and the contribution of implicit learning would be modulated by the form of error feedback. By directly assaying aiming direction, we could identify the time course of the explicit component and, via subtraction, isolate the implicit component of learning. There were marked differences in the time courses of explicit and implicit contributions to learning. Explicit learning, driven by target error, was achieved by initially large then smaller explorations of aiming direction biased toward the correct solution. In contrast, implicit learning, driven by a sensory-prediction error, was slow and monotonic. Continuous error feedback reduced the amplitude of explicit learning and increased the contribution of implicit learning. The presence of instruction slightly increased the rate of initial learning and only had a subtle effect on implicit learning. We conclude that visuomotor adaptation, even in the absence of instruction, results from the interplay between explicit learning driven by target error and implicit learning of a forward model driven by prediction error.

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.

In sensorimotor adaptation, explicit cognitive strategies are thought to be unnecessary because the motor system implicitly corrects performance throughout training. This seemingly automatic process involves computing an error between the planned movement and actual feedback of the movement. When explicitly provided with an effective strategy to overcome an experimentally induced visual perturbation, people are immediately successful and regain good task performance. However, as training continues, their accuracy gets worse over time. This counterintuitive result has been attributed to the independence of implicit motor processes and explicit cognitive strategies. The cerebellum has been hypothesized to be critical for the computation of the motor error signals that are necessary for implicit adaptation. We explored this hypothesis by testing patients with cerebellar degeneration on a motor learning task that puts the explicit and implicit systems in conflict. Given this, we predicted that the patients would be better than controls in maintaining an effective strategy assuming strategic and adaptive processes are functionally and neurally independent. Consistent with this prediction, the patients were easily able to implement an explicit cognitive strategy and showed minimal interference from undesirable motor adaptation throughout training. These results further reveal the critical role of the cerebellum in an implicit adaptive process based on movement errors and suggest an asymmetrical interaction of implicit and explicit processes.

Brudner SN, Kethidi N, Graeupner D, Ivry RB, Taylor JA. Delayed feedback during sensorimotor learning selectively disrupts adaptation but not strategy use. J Neurophysiol 115: 1499 -1511, 2016. First published January 20, 2016 doi:10.1152/jn.00066.2015.-In sensorimotor adaptation tasks, feedback delays can cause significant reductions in the rate of learning. This constraint is puzzling given that many skilled behaviors have inherently long delays (e.g., hitting a golf ball). One difference in these task domains is that adaptation is primarily driven by error-based feedback, whereas skilled performance may also rely to a large extent on outcome-based feedback. This difference suggests that error-and outcome-based feedback may engage different learning processes, and these processes may be associated with different temporal constraints. We tested this hypothesis in a visuomotor adaptation task. Error feedback was indicated by the terminal position of a cursor, while outcome feedback was indicated by points. In separate groups of participants, the two feedback signals were presented immediately at the end of the movement, after a delay, or with just the error feedback delayed. Participants learned to counter the rotation in a similar manner regardless of feedback delay. However, the aftereffect, an indicator of implicit motor adaptation, was attenuated with delayed error feedback, consistent with the hypothesis that a different learning process supports performance under delay. We tested this by employing a task that dissociates the contribution of explicit strategies and implicit adaptation. We find that explicit aiming strategies contribute to the majority of the learning curve, regardless of delay; however, implicit learning, measured over the course of learning and by aftereffects, was significantly attenuated with delayed error-based feedback. These experiments offer new insight into the temporal constraints associated with different motor learning processes.forward model; internal model; motor adaptation; motor learning; reinforcement learning LEARNING TO LINK ACTIONS with their respective outcomes is necessary for successfully interacting with the world. Feedback delays can present a significant problem in learning actionoutcome associations, yet such delays are ubiquitous in motor control. Feedback from proprioception and vision are inherently delayed because of neural transmission and integration time. Even if these delays are relatively short, the control system faces a significant problem due to potentially destabilizing effects arising from body and environmental dynamics.One prominent solution for handling delayed feedback in the sensorimotor learning literature centers on the idea of an adaptive forward model, a representation that allows the system to anticipate or predict the sensory consequences of an action through sensory-prediction errors (Wolpert and Miall 1996). However, various lines of evidence indicate that this solution is subject to severe temporal constraints. Modest additional delays ...

Generalization is a fundamental aspect of behavior, allowing for the transfer of knowledge from one context to another. The details of this transfer are thought to reveal how the brain represents what it learns. Generalization has been a central focus in studies of sensorimotor adaptation, and its pattern has been well characterized: Learning of new dynamic and kinematic transformations in one region of space tapers off in a Gaussian-like fashion to neighboring untrained regions, echoing tuned population codes in the brain. In contrast to common allusions to generalization in cognitive science, generalization in visually guided reaching is usually framed as a passive consequence of neural tuning functions rather than a cognitive feature of learning. While previous research has presumed that maximum generalization occurs at the instructed task goal or the actual movement direction, recent work suggests that maximum generalization may occur at the location of an explicitly accessible movement plan. Here we provide further support for plan-based generalization, formalize this theory in an updated model of adaptation, and test several unexpected implications of the model. First, we employ a generalization paradigm to parameterize the generalization function and ascertain its maximum point. We then apply the derived generalization function to our model and successfully simulate and fit the time course of implicit adaptation across three behavioral experiments. We find that dynamics predicted by plan-based generalization are borne out in the data, are contrary to what traditional models predict, and lead to surprising implications for the behavioral, computational, and neural characteristics of sensorimotor adaptation. The pattern of generalization is thought to reveal how the motor system represents learned actions. Recent work has made the intriguing suggestion that maximum generalization in sensorimotor adaptation tasks occurs at the location of the learned movement plan. Here we support this interpretation, develop a novel model of motor adaptation that incorporates plan-based generalization, and use the model to successfully predict surprising dynamics in the time course of adaptation across several conditions.

There has been renewed interest in the role of strategies in sensorimotor learning. The combination of new behavioral methods and computational methods has begun to unravel the interaction between processes related to strategic control and processes related to motor adaptation. These processes may operate on very different error signals. Strategy learning is sensitive to goal-based performance error. In contrast, adaptation is sensitive to prediction errors between the desired and actual consequences of a planned movement. The former guides what the desired movement should be, whereas the latter guides how to implement the desired movement. Whereas traditional approaches have favored serial models in which an initial strategy-based phase gives way to more automatized forms of control, it now seems that strategic and adaptive processes operate with considerable independence throughout learning, although the relative weight given the two processes will shift with changes in performance. As such, skill acquisition involves the synergistic engagement of strategic and adaptive processes.

When humans experience externally induced errors in a movement, the motor system's feedback control compensates for those errors within the movement. The motor system's predictive control then uses information about those errors to inform future movements. The role of attention in these two distinct motor processes is unclear. Previous experiments have revealed a role for attention in motor learning over the course of many movements; however, these experimental paradigms do not determine how attention influences within-movement feedback control versus across-movement adaptation. Here we develop a dual-task paradigm, consisting of movement and audio tasks, which can differentiate and expose attention's role in these two processes of motor control. Over the course of several days, subjects performed horizontal reaching movements, with and without the audio task; movements were occasionally subjected to transient force perturbations. On movements with a force perturbation, subjects compensated for the force-induced movement errors, and on movements immediately after the force perturbation subjects exhibited adaptation. On every movement trial, subjects performed a two-tone frequency-discrimination task. The temporal specificity of the frequency-discrimination task allowed us to divide attention within and across movements. We find that divided attention did not impair the within-movement feedback control of the arm, but did reduce subsequent movement adaptation. We suggest that the secondary task interfered with the encoding and transformation of errors into changes in predictive control.

People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low-and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.