Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound) if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates.

Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

Two different compensatory mechanisms are engaged when the nervous system senses errors during a reaching movement. First, on-line feedback control mechanisms produce in-flight corrections to reduce errors in the on-going movement. Second, these errors modify the internal model with which the motor plan is transformed into motor commands for the subsequent movements. What are the neural mechanisms of these compensatory systems? In a previous study, we reported that while on-line error correction was disturbed in patients with Huntington's disease (HD), it was largely intact in patients with cerebellar degeneration. Here we altered dynamics of reaching and studied the effect of error in one trial on the motor commands that initiated the subsequent trial. We observed that in patients with cerebellar degeneration, motor commands changed from trial-to-trial by an amount that was comparable to control subjects. However, these changes were random and were uninformed by the error in the preceding trial. In contrast, the change in motor commands of HD patients was strongly related to the error in the preceding trial. This error-dependent change had a sensitivity that was comparable to healthy controls. As a result, HD patients exhibited no significant deficits in adapting to novel arm dynamics, whereas cerebellar subjects were profoundly impaired. These results demonstrate a double dissociation between on-line and trial-to-trial error correction suggesting that these compensatory mechanisms have distinct neural bases that can be differentially affected by disease.

Trial-to-trial variability in the execution of movements and motor skills is ubiquitous, and widely considered to be the unwanted consequence of a ‘noisy’ nervous system. However, recent studies have suggested that motor variability may also be a feature of how sensorimotor systems operate and learn. This view, rooted in reinforcement learning theory, equates motor variability with purposeful exploration of motor space that, when coupled with reinforcement, can drive motor learning. Here we review studies that explore the relationship between motor variability and motor learning both in humans and animal models. We discuss neural circuit mechanisms that underlie the generation and regulation of motor variability and consider the implications that this work has for our understanding of motor learning.