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From a baby’s babbling to a songbird practicing a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species, neural firing, gait patterns, and reaching behaviour. Past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here we test a fundamentally different idea—that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human reaching experiments we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that exploratory random walk behaviour emerges by utilizing knowledge of movement variability to update intended reach aim towards recently reinforced motor actions. This mechanism leads to active and continuous exploration of the solution manifold, currently thought by prominent theories to arise passively. The ability to continually explore muscle, joint, and task redundant solution manifolds is beneficial while acting in uncertain environments, during motor development, or when recovering from a neurological disorder to discover and learn new motor actions.

Many contexts in motor learning require a learner to change from an existing movement solution to a novel movement solution to perform the same task. Recent evidence has pointed to motor variability prior to learning as a potential marker for predicting individual differences in motor learning. However, it is not known if this variability is predictive of the ability to adopt a new movement solution for the same task. Here, we examined this question in the context of a redundant precision task requiring control of motor variability. Fifty young adults learned a precision task that involved throwing a virtual puck toward a target using both hands. Because the speed of the puck depended on the sum of speeds of both hands, this task could be achieved using multiple solutions. Participants initially performed a baseline task where there was no constraint on the movement solution, and then performed a novel task where they were constrained to adopt a specific movement solution requiring asymmetric left and right hand speeds. Results showed that participants were able to learn the new solution, and this change was associated with changes in both the amount and structure of variability. However, individual differences in baseline motor variability were only weakly correlated with initial and final task performance when using the new solution, with greater variability being associated with higher errors. We also found a strong specificity component – initial variability when using the new solution was highly correlated with final task performance with the new solution, but once again, higher variability was associated with greater errors. These results suggest that motor variability is not necessarily indicative of flexibility and highlight the need to consider the task context in determining the relation between motor variability and learning.

Understanding the use of haptic assistance to facilitate motor learning is a critical issue, especially in the context of tasks requiring control of motor variability. However, the question of how haptic assistance should be designed in tasks with redundancy, where multiple solutions are available, is currently unknown. Here we examined the effect of haptic assistance that either allowed or restricted the use of redundant solutions on the learning of a bimanual steering task. 60 college-aged participants practiced steered a single cursor placed in between their hands along a smooth W-shaped track of a certain width as quickly as possible. Haptic assistance was either applied at the ‘task’ level using a force channel that only constrained the cursor to the track, allowing for the use of different hand trajectories, or (ii) the ‘individual effector’ level using a force channel that constrained each hand to a specific trajectory. In addition, we also examined the effect of ‘fading’ – i.e., decreasing assistance with practice to reduce dependence on haptic assistance. Results showed all groups improved with practice - however, groups with haptic assistance at the individual effector level performed worse than those at the task level. Moreover, fading of assistance did not offer learning benefits over constant assistance. Overall, the results suggest that haptic assistance is not effective for motor learning when it restricts the use of redundant solutions.