Task errors contribute to implicit aftereffects in sensorimotor adaptation.

Perturbations of sensory feedback evoke sensory prediction errors (discrepancies between predicted and actual sensory outcomes of movements), and reward prediction errors (discrepancies between predicted rewards and actual rewards). When our task is to hit a target, we expect to succeed in hitting the target, and so we experience a reward prediction error if the perturbation causes us to miss it. These discrepancies between intended task outcomes and actual task outcomes, termed "task errors," are thought to drive the use of strategic processes to restore success, although their role is incompletely understood. Here, as participants adapted to a 30° rotation of cursor feedback representing hand position, we investigated the role of task errors in sensorimotor adaptation: during target-reaching, we either removed task errors by moving the target mid-movement to align with cursor feedback of hand position, or enforced task error by moving the target away from the cursor feedback of hand position, by 20-30° randomly (clockwise in half the trials, counterclockwise in half the trials). Removing task errors not only reduced the extent of adaptation during exposure to the perturbation, but also reduced the amount of post-adaptation aftereffects that persisted despite explicit knowledge of the perturbation removal. Hence, task errors contribute to implicit adaptation resulting from sensory prediction errors. This suggests that the system which predicts the sensory consequences of actions via exposure to sensory prediction errors is also sensitive to reward prediction errors.

Updating of Implicit Adaptation Processes through Erroneous Numeric Feedback.

There is debate about how implicit and explicit processes interact in sensorimotor adaptation, implicating how error signals drive learning. Target error information is thought to primarily influence explicit processes, therefore manipulations to the veracity of this information should impact adaptation but not implicit recalibration (i.e. after-effects). Thirty participants across three groups initially adapted to rotated cursor feedback. Then we manipulated numeric target error through knowledge of results (KR) feedback, where groups practised with correct or incorrect (+/-15°) numeric KR. Participants adapted to erroneous KR, but only the KR + 15 group showed augmented implicit recalibration, evidenced by larger after-effects than before KR exposure. In the presence of sensory prediction errors, target errors modulated after-effects, suggesting an interaction between implicit and explicit processes.

Implicit Sensorimotor Adaptation Proceeds in Absence of Movement Execution.

In implicit sensorimotor adaptation, a mismatch between the predicted and actual sensory feedback results in a sensory prediction error (SPE). Sensory predictions have long been thought to be linked to descending motor commands, implying a necessary contribution of movement execution to adaptation. However, recent work has shown that mere motor imagery (MI) also engages predictive mechanisms, opening up the possibility that MI might be sufficient to drive implicit adaptation. In a within-subject design in humans (n = 30), implicit adaptation was assessed in a center-out reaching task, following a single exposure to a visuomotor rotation. It was hypothesized that performing MI of a reaching movement while being provided with an animation of rotated visual feedback (MI condition) would lead to postrotation biases (PRBs) similar to those observed when the movement is executed (Execution condition). Results revealed that both the MI and Execution conditions led to significant directional biases following rotated trials. Yet the magnitude of these biases was significantly larger in the Execution condition. To further probe the contribution of MI to adaptation, a Control condition was conducted in which participants were presented with the same rotated visual animation as in the MI condition, but in which they were prevented from performing MI. Surprisingly, significant biases were also observed in the Control condition, suggesting that MI per se may not have accounted for adaptation. Overall, these results suggest that implicit adaptation can be partially supported by processes other than those that strictly pertain to generating motor commands, although movement execution does potentiate it.

The cerebellum for jocks and nerds alike.

Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains.