Mussa-Ivaldi F A, Danziger Z
Department of Physiology, Northwestern University, The Feinberg School of Medicine, M211/303 E. Chicago Ave., Chicago, IL 60611, USA.
J Physiol Paris. 2009 Sep-Dec;103(3-5):263-75. doi: 10.1016/j.jphysparis.2009.08.009. Epub 2009 Aug 7.
Studies of motor adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. One of the most fundamental elements of our environment is space itself. This article focuses on the notion of Euclidean space as it applies to common sensory motor experiences. Starting from the assumption that we interact with the world through a system of neural signals, we observe that these signals are not inherently endowed with metric properties of the ordinary Euclidean space. The ability of the nervous system to represent these properties depends on adaptive mechanisms that reconstruct the Euclidean metric from signals that are not Euclidean. Gaining access to these mechanisms will reveal the process by which the nervous system handles novel sophisticated coordinate transformation tasks, thus highlighting possible avenues to create functional human-machine interfaces that can make that task much easier. A set of experiments is presented that demonstrate the ability of the sensory-motor system to reorganize coordination in novel geometrical environments. In these environments multiple degrees of freedom of body motions are used to control the coordinates of a point in a two-dimensional Euclidean space. We discuss how practice leads to the acquisition of the metric properties of the controlled space. Methods of machine learning based on the reduction of reaching errors are tested as a means to facilitate learning by adaptively changing he map from body motions to controlled device. We discuss the relevance of the results to the development of adaptive human-machine interfaces and optimal control.
对运动适应确定性力模式的研究揭示了运动控制系统形成和使用环境预测表征的能力。我们环境中最基本的元素之一就是空间本身。本文聚焦于欧几里得空间这一概念，因为它适用于常见的感觉运动体验。从我们通过神经信号系统与世界互动这一假设出发，我们观察到这些信号本身并不具备普通欧几里得空间的度量属性。神经系统表征这些属性的能力取决于从非欧几里得信号中重建欧几里得度量的适应性机制。了解这些机制将揭示神经系统处理新颖复杂坐标变换任务的过程，从而突出创建能使该任务变得更容易的功能性人机界面的可能途径。本文展示了一组实验，这些实验证明了感觉运动系统在新颖几何环境中重新组织协调的能力。在这些环境中，身体运动的多个自由度被用于控制二维欧几里得空间中一个点的坐标。我们讨论了练习如何导致对受控空间度量属性的习得。基于减少到达误差的机器学习方法作为一种通过自适应改变从身体运动到受控设备的映射来促进学习的手段进行了测试。我们讨论了这些结果与自适应人机界面开发和最优控制的相关性。
