Individuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory prediction error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why do individuals with cerebellar damage not come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, before executing a movement on each trial, the participants verbally reported their intended aiming location. Compared with healthy control participants, participants with spinocerebellar ataxia displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly () or gradually (). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability (). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory prediction error-based learning but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials. Individuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory prediction errors to update an internal model. Here we show that these individuals also have difficulty in discovering an aiming solution to overcome their adaptation deficit, suggesting a new role for the cerebellum in sensorimotor adaptation tasks.

We routinely make fine motor adjustments to maintain optimal motor performance. These adaptations have been attributed to both implicit, error-based mechanisms, and explicit, strategy-based mechanisms. However, little is known about the neural basis of implicit vs. explicit learning. Here, we aimed to use anodal transcranial direct current stimulation (tDCS) to probe the relationship between different brain regions and learning mechanisms during a visuomotor adaptation task in humans. We hypothesized that anodal tDCS over the cerebellum (CB) should increase implicit learning while anodal tDCS over the dorsolateral prefrontal cortex (dlPFC), a region associated with higher-level cognition, should facilitate explicit learning. Using a horizontal visuomotor adaptation task that measures explicit/implicit contributions to learning (Taylor et al., 2014), we found that dlPFC stimulation significantly improved performance compared to the other groups, and weakly increased explicit learning. However, CB stimulation had no effects on either target error or implicit learning. Previous work showed variable CB stimulation effects only on a vertical visuomotor adaptation task (Jalali et al., 2017), so in Experiment 2, we conducted the same study using a vertical context to see if we could find effects of CB stimulation. We found only weak effects of CB stimulation on target error and implicit learning, and now the dlPFC effect did not replicate. To resolve this discrepancy, in Experiment 3, we examined the effect of context (vertical vs. horizontal) on implicit and explicit contributions and found that individuals performed significantly worse and used greater implicit learning in the vertical screen condition compared to the horizontal screen condition. Across all experiments, however, there was high inter-individual variability, with strong influences of a few individuals, suggesting that these effects are not consistent across individuals. Overall, this work provides preliminary support for the idea that different neural regions can be engaged to improve visuomotor adaptation, but shows that each region's effects are highly context-dependent and not clearly dissociable from one another. This holds implications especially in neurorehabilitation, where an intact neural region could be engaged to potentially compensate if another region is impaired. Future work should examine factors influencing interindividual variability during these processes.

Highlights d Humans devalue choices less following execution versus selection errors d Reward prediction errors in the striatum are attenuated following execution errors d Different error classes have distinct neural signatures

To accomplish effective motor control, the brain contains an internal forward model that predicts the expected sensory consequence of a motor command. When this prediction is inaccurate, a sensory prediction error is produced which adapts the forward model to make more accurate predictions of future movements. Other types of errors, such as task performance errors or reward, play less of a role in adapting a forward model. This raises the following question: What unique information is conveyed by the sensory prediction error that results in forward model adaptation? sensory prediction errors typically contain both the magnitude and direction of the error, but it is unclear if both components are necessary for adaptation or a single component is sufficient. In this article, we address this by having participants learn to counter a visuomotor rotation, which induces an angular mismatch between movements of the hand and visual feedback. We manipulated the information content of the visual feedback, in the form of a line, which accurately represented only the magnitude (distance), direction, or both magnitude and direction, of the virtual cursor relative to the target. We demonstrate that sensorimotor adaptation does not occur, or is minimal, when feedback is limited to information about the magnitude of an error. In contrast, sensorimotor adaptation is present when feedback is limited only to the direction of an error or when it contains combined direction and magnitude information. This result stands in contrast to current computational models of cerebellar-based sensorimotor adaptation that use error magnitude to drive adaptation. (PsycINFO Database Record

A skilled pianist can verbally report a particular piece's sequence of notes or she can execute the sequence with her fingers. There are two types of memory processes involved here: a memory that can be declared (naming the notes) and a procedural memory that can be implemented (pressing the keys in the correct order).The study of patients with medial temporal lobe lesions, such as HM, provides compelling evidence that these processes are neurally distinct. Patients such as HM are able to learn new motor skills and maintain those skills over time, without being aware that they have learned the skill (Scoville and Milner, 1957;Corkin, 1968). These studies were foundational in cognitive science, helping set forth productive literature exploring the domain and mechanisms associated with declarative and procedural learning.The serial reaction time task (SRTT) was developed as a tool to examine the operation of procedural and declarative processes simultaneously (Nissen and Bullemer, 1987). In the SRTT, participants are presented a series of visual cues at different spatial locations and instructed to press corresponding buttons. The series of stimuli can be random or can follow a repeated sequence. When the series follows a sequence, participants show behavioral evidence of learning, in that their reaction time (RT) decreases. Improvements in RT occur even when participants are unable to verbalize what they learned, or even to be aware that the stimuli followed a sequence. Subjects also can gain explicit knowledge of a sequence if subtle changes are made to the task, such as increasing repetitions or the regularity of the stimulus pattern.A cognitive task cannot be taken as a pure measure of a single cognitive mechanism (Jacoby, 1991), however, and SRTT is no exception. In the example of sequence learning, both declarative and procedural processes are in operation. These differing components can interact at different stages during encoding, storage, consolidation, and retrieval. The degree to which each process contributes to a given task can only be teased apart with careful experimental manipulations. In the case of the SRTT, the involvement of distinct memory systems suggests that introducing a competing memory task (for either system) after the initial learning should lead to competition with the corresponding component of the sequence learning task, and would therefore lead to decreased performance at later stages. In practice, the memory tasks are performed only after the motor task is complete, suggesting that any interaction found is due to an effect on consolidation of the memory, rather than on attention or encoding demands of a dual task.In a series of experiments using the SRTT and word list learning, Brown and Robertson (2007) showed that there is an interaction between declarative and procedural memories during consolidation, and that this effect was bidirectional. In these studies, the SRTT had a substantial declarative component. Participants could verbally report the sequence of button presses, just...