Motion Analysis Laboratory, Kennedy Krieger Institute, Baltimore, Maryland; and.
J Neurophysiol. 2014 Mar;111(5):969-76. doi: 10.1152/jn.00513.2013. Epub 2013 Dec 11.
Visual input provides vital information for helping us modify our walking pattern. For example, artificial optic flow can drive changes in step length during locomotion and may also be useful for augmenting locomotor training for individuals with gait asymmetries. Here we asked whether optic flow could modify the acquisition of a symmetric walking pattern during split-belt treadmill adaptation. Participants walked on a split-belt treadmill while watching a virtual scene that produced artificial optic flow. For the Stance Congruent group, the scene moved at the slow belt speed at foot strike on the slow belt and then moved at the fast belt speed at foot strike on the fast belt. This approximates what participants would see if they moved over ground with the same walking pattern. For the Stance Incongruent group, the scene moved fast during slow stance and vice versa. In this case, flow speed does not match what the foot is experiencing, but predicts the belt speed for the next foot strike. Results showed that the Stance Incongruent group learned more quickly than the Stance Congruent group even though each group learned the same amount during adaptation. The increase in learning rate was primarily driven by changes in spatial control of each limb, rather than temporal control. Interestingly, when this alternating optic flow pattern was presented alone, no adaptation occurred. Our results demonstrate that an unnatural pattern of optic flow, one that predicts the belt speed on the next foot strike, can be used to enhance learning rate during split-belt locomotor adaptation.
视觉输入为我们调整步行模式提供了重要信息。例如,人工光流可以在运动中改变步长,对于步态不对称的个体进行运动训练增强也可能是有用的。在这里,我们想知道光流是否可以在分带跑步机适应过程中改变对称步行模式的习得。参与者在分带跑步机上行走,同时观看产生人工光流的虚拟场景。对于站立一致组,场景在慢带脚着地时以慢带速度移动,然后在快带脚着地时以快带速度移动。这近似于参与者以相同的步行模式在地面上移动时会看到的情况。对于站立不一致组,场景在慢站立时快速移动,反之亦然。在这种情况下,流速与脚的体验不匹配,但预测了下一个脚着地的带速。结果表明,站立不一致组的学习速度比站立一致组快,尽管每组在适应过程中学习的量相同。学习率的提高主要是由每个肢体的空间控制变化驱动的,而不是由时间控制驱动的。有趣的是,当单独呈现这种交替光流模式时,不会发生适应。我们的结果表明,一种不自然的光流模式,即预测下一个脚着地的带速,可以用于增强分带运动适应过程中的学习率。