Adaptation and generalization in acceleration-dependent force fields
Adaptation and generalization in acceleration-dependent force fields
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DOI：
10.1007/s00221-005-0163-2
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发表时间：
2006-03-01
影响因子：
2
通讯作者：
Shadmehr, R
中科院分区：
文献类型：
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作者：
Hwang, EJ;Smith, MA;Shadmehr, R
我们手中握住的任何被动刚性惯性物体，例如网球拍，都会在手臂上施加一个取决于肢体位置、速度和加速度的力场。这个场的一个基本特征是，由加速度和速度引起的力在肢体的固有坐标中是线性可分离的。为了用一组基本元素来学习这种动力学，如果对肢体速度敏感的基本元素对加速度不敏感，则控制系统将正确地泛化，从而最佳地执行，反之亦然。然而，在哺乳动物神经系统中，像肌梭这样的本体感觉传感器编码了肢体状态的所有组成部分的非线性组合，对速度的敏感性主导着对加速度的敏感性。因此，本体感觉空间中的肢体状态不是线性可分离的，尽管这种分离是形成惯性对象模型的控制系统的理想特性。在建立肢体动力学的内部模型时，大脑是使用最适合控制惯性物体的表示法，还是使用与外围传感器测量肢体状态密切相关的表示法？在这里，我们表明，在人类中，在依赖于加速度的领域中的到达运动的概括模式与为控制惯性物体而优化的基本元素强烈不一致。与机器人控制器不同，机器人控制器对自然世界的动力学进行建模，并独立表示速度和加速度，而人们学习的内部动力学模型似乎植根于本体感觉的特性，对肌肉激活模式做出非线性反应，并且比加速度更能表示速度。
Any passive rigid inertial object that we hold in our hand, e.g., a tennis racquet, imposes a field of forces on the arm that depends on limb position, velocity, and acceleration. A fundamental characteristic of this field is that the forces due to acceleration and velocity are linearly separable in the intrinsic coordinates of the limb. In order to learn such dynamics with a collection of basis elements, a control system would generalize correctly and therefore perform optimally if the basis elements that were sensitive to limb velocity were not sensitive to acceleration, and vice versa. However, in the mammalian nervous system proprioceptive sensors like muscle spindles encode a nonlinear combination of all components of limb state, with sensitivity to velocity dominating sensitivity to acceleration. Therefore, limb state in the space of proprioception is not linearly separable despite the fact that this separation is a desirable property of control systems that form models of inertial objects. In building internal models of limb dynamics, does the brain use a representation that is optimal for control of inertial objects, or a representation that is closely tied to how peripheral sensors measure limb state? Here we show that in humans, patterns of generalization of reaching movements in acceleration-dependent fields are strongly inconsistent with basis elements that are optimized for control of inertial objects. Unlike a robot controller that models the dynamics of the natural world and represents velocity and acceleration independently, internal models of dynamics that people learn appear to be rooted in the properties of proprioception, nonlinearly responding to the pattern of muscle activation and representing velocity more strongly than acceleration.