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首页> 外文期刊>BMC Neuroscience >Learning course adjustments during arm movements with reversed sensitivity derivatives
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Learning course adjustments during arm movements with reversed sensitivity derivatives

机译：反向灵敏度导数学习手臂运动过程中的课程调整

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Background To learn, a motor system needs to know its sensitivity derivatives, which quantify how its neural commands affect motor error. But are these derivatives themselves learned, or are they known solely innately? Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback. In its simplest form, the theory says that each control system has a single, adjustable estimate of its sensitivity derivatives which affects all aspects of its task, e.g. if you learn to reach to mirror-reversed targets then your revised estimate should reverse not only your initial aiming but also your online course adjustments when the target jumps in mid-movement. Methods Human subjects bent a joystick to move a cursor to a target on a computer screen, but the cursor's motion was reversed relative to the joystick's. The target jumped once during each movement. Subjects had up to 4000 trials to practice aiming and responding to target jumps. Results All subjects learned to reverse both initial aiming and course adjustments. Conclusions Our study confirms that sensitivity derivatives can be relearned. It is consistent with the idea of a single, all-purpose estimate of those derivatives; and it suggests that the estimate is a function of context, as one would expect given that the true sensitivity derivatives may vary with the state of the controlled system, the target, and the motor commands.
机译：背景技术要学习，电机系统需要了解其灵敏度导数，以量化其神经命令如何影响电机误差。但是这些衍生物本身是被学习的，还是它们本身是天生的？在这里，我们测试了一种新的理论，即基于感觉反馈，大脑对敏感度导数的估计是可修改的。该理论以最简单的形式说，每个控制系统对其灵敏度导数有一个可调整的估计，这会影响其任务的各个方面，例如：如果您学习达到反向的目标，则当目标在中间运动时跳跃时，修订后的估算不仅应逆转最初的目标，还应逆转您的在线路线调整。方法人类受试者弯曲操纵杆以将光标移动到计算机屏幕上的目标，但是光标的运动相对于操纵杆相反。在每次移动中，目标跳一次。受试者进行了多达4000次试验，以练习瞄准和对目标跳跃的反应。结果所有受试者都学会了逆转最初的瞄准和路线调整。结论我们的研究证实可以重新获得敏感性衍生物。这与对这些衍生工具进行单一，通用估算的想法是一致的。这表明估计值是上下文的函数，因为真实的灵敏度导数可能会随受控系统，目标和电动机命令的状态而变化，因此可以预期。

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