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首页> 外文期刊>Journal of Neurophysiology >Learning to shape virtual patient locomotor patterns: internal representations adapt to exploit interactive dynamics
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Learning to shape virtual patient locomotor patterns: internal representations adapt to exploit interactive dynamics

机译：学习塑造虚拟患者运动模式：内部表示适应挖掘交互式动态

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This work aimed to understand the sensorimotor processes used by humans when learning how to manipulate a virtual model of locomotor dynamics. Prior research shows that when interacting with novel dynamics humans develop internal models that map neural commands to limb motion and vice versa. Whether this can be extrapolated to locomotor rehabilitation, a continuous and rhythmic activity that involves dynamically complex interactions, is unknown. In this case, humans could default to model-free strategies. These competing hypotheses were tested with a novel interactive locomotor simulator that reproduced the dynamics of hemiparetic gait. A group of 16 healthy subjects practiced using a small robotic manipulandum to alter the gait of a virtual patient (VP) that had an asymmetric locomotor pattern modeled after stroke survivors. The point of interaction was the ankle of the VP's affected leg. and the goal was to make the VP's gait symmetric. Internal model formation was probed with unexpected force channels and null force fields. Generalization was assessed by changing the target locomotor pattern and comparing outcomes with a second group of 10 naive subjects who did not practice the initial symmetric target pattern. Results supported the internal model hypothesis with aftereffects and generalization of manipulation skill. Internal models demonstrated refinements that capitalized on the natural pendular dynamics of human locomotion. This work shows that despite the complex interactive dynamics involved in shaping locomotor patterns. humans nevertheless develop and use internal models that are refined with experience.
机译：这项工作旨在了解人类在学习如何操纵运动动态的虚拟模型时使用的感觉电流过程。现有研究表明，与新型动态交互时，人类开发映射神经命令的内部模型，以肢体运动，反之亦然。无论这是否可以推断为运动康复，涉及动态复杂相互作用的连续和节律活动是未知的。在这种情况下，人类可以默认为无模型策略。用新型的交互式运动模拟器测试这些竞争的假设，用于复制偏瘫步态的动态。使用小型机器人Manipulandum实践的一组16个健康受试者，以改变虚拟患者（vp）的步态，所述虚拟患者（vp）具有在中风幸存者后建模的不对称运动模式。互动点是VP受影响的腿的脚踝。目标是使VP的步态对称。用意外的力通道和空力场探测内部模型形成。通过改变目标运动模式并将结果与​​第二组10天真的受试者进行比较来评估泛化，该概念没有练习初始对称目标模式。结果支持内部模型假设，具有后果和操纵技能的泛化。内部模型显示出资本化人类运动的自然垂体动态的细化。这项工作表明，尽管成形机器人模式中涉及复杂的交互式动态。人类仍然发展和使用具有体验的内部模型。

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