Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.
J Neurophysiol. 2020 Apr 1;123(4):1342-1354. doi: 10.1152/jn.00687.2019. Epub 2020 Mar 4.
From reaching to walking, real-life experience suggests that people can generalize between motor behaviors. One possible explanation for this generalization is that real-life behaviors often challenge our balance. We propose that the exacerbated body motions associated with balance-challenged whole body movements increase the value to the nervous system of using a comprehensive internal model to control the task. Because it is less customized to a specific task, a more comprehensive model is also a more generalizable model. Here we tested the hypothesis that challenging balance during adaptation would increase generalization of a newly learned internal model. We encouraged participants to learn a new internal model using prism lenses that created a new visuomotor mapping. Four groups of participants adapted to prisms while performing either a standing-based reaching or precision walking task, with or without a manipulation that challenged balance. To assess generalization after the adaptation phase, participants performed a single trial of each of the other groups' tasks without prisms. We found that both the reaching and walking balance-challenged groups showed significantly greater generalization to the equivalent, nonadapted task than the balance-unchallenged groups. Additionally, we found some evidence that all groups generalized across tasks, for example, from walking to reaching and vice versa, regardless of balance manipulation. Overall, our results demonstrate that challenging balance increases the degree to which a newly learned internal model generalizes to untrained movements. Real-life experience indicates that people can generalize between motor behaviors. Here we show that challenging balance during the learning of a new internal model increases the degree of generalization to untrained movements for both reaching and walking tasks. These results suggest that the effects of challenging balance are not specific to the task but instead apply to motor learning more broadly.
从伸手到行走，现实生活中的经验表明，人们可以在运动行为之间进行泛化。这种泛化的一个可能解释是，现实生活中的行为经常挑战我们的平衡。我们提出，与平衡挑战的全身运动相关的身体动作加剧会增加使用综合内部模型来控制任务的神经系统的价值。由于它不太针对特定任务，因此更全面的模型也是更可泛化的模型。在这里，我们测试了这样一个假设，即在适应过程中挑战平衡会增加新学习的内部模型的泛化程度。我们鼓励参与者使用创建新的视动映射的棱镜镜头来学习新的内部模型。四组参与者在执行基于站立的伸手或精确行走任务时适应棱镜，无论是否进行平衡挑战的操作。为了评估适应阶段后的泛化，参与者在没有棱镜的情况下进行了其他三组任务的单一试验。我们发现，与平衡不受挑战的组相比，伸手和行走平衡受挑战的组在等效的非适应任务中表现出明显更大的泛化。此外，我们发现一些证据表明，无论平衡操作如何，所有组都可以跨任务进行泛化，例如从行走到伸手，反之亦然。总的来说，我们的结果表明，平衡挑战会增加新学习的内部模型对未经训练的运动的泛化程度。现实生活中的经验表明，人们可以在运动行为之间进行泛化。在这里，我们表明，在学习新的内部模型时，平衡挑战会增加对伸手和行走任务的未经训练运动的泛化程度。这些结果表明，平衡挑战的影响不是特定于任务的，而是更广泛地适用于运动学习。
