Learning new locomotor patterns using repetition and reward
Date
2023
Authors
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Publisher
University of Delaware
Abstract
In the 2021 Olympic games, Sydney McLaughlin won the gold medal in the 400-meter hurdle race by learning to become equally skilled at jumping over the hurdles with both her dominant and her non-dominant legs. This task is accomplished through motor learning, the ability to acquire and retain motor skills with practice. Motor learning is not a single process, but the combination of multiple processes that, together, contribute to overall acquisition of a new motor skill. Repetition and reward form the basis for two of these learning processes called use-dependent learning and reinforcement learning, respectively. Since these processes are important to the formation of motor memories, understanding the constraints and limitations of use-dependent and reinforcement learning is critical. However, use-dependent learning and reinforcement learning have never been isolated and explicitly tested in walking. In the current work we directly tested the contribution of repetition and reward to locomotor learning in young healthy individuals with intact neurologic systems. ☐ Each motor learning process is driven by specific signals that are processed in distinct anatomical locations. These signals are created experimentally by manipulating things like task conditions, feedback, instructions, or other task parameters. Here, we used visual feedback during treadmill walking to induce learning of novel gait patterns, manipulating specifics aspects of the task and feedback to isolate and compare motor learning processes. ☐ In Aim 1, we found that repetition alone can bias future gait patterns, suggesting that use-dependent learning plays an important, and previously under-appreciated role in locomotor learning. Since all movements are inherently variable, in Aim 2, we asked how consistent movements must be to take advantage of repeated practice and use-dependent learning. Our findings further clarified this repetition-based process, demonstrating that movement variability constrains use-dependent locomotor learning. In Aim 3, we found that individuals were able to acquire and retain a new walking pattern using reinforcement learning, doing so by exploring a range of possible actions. Additionally, reinforcement learning resulted in better motor memories than a group using target error feedback. Overall, the current work demonstrated that locomotor learning is multifaceted, with repetition and reward providing important contributions to both acquiring and remembering novel gait patterns. Consequently, this work emphasizes the importance of a holistic view of motor learning in gait, motivating future work that considers the range of known locomotor learning processes in both healthy and clinical populations.
Description
Keywords
Gait, Locomotor learning, Motor learning, Reinforcement learning, Repetition, Use-dependent learning