Giese M A, Dijkstra T M, Schöner G, Gielen C C
Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany.
Biol Cybern. 1996 May;74(5):427-37. doi: 10.1007/BF00206709.
Human subjects standing in a sinusoidally moving visual environment display postural sway with characteristic dynamical properties. We analyzed the spatiotemporal properties of this sway in an experiment in which the frequency of the visual motion was varied. We found a constant gain near 1, which implies that the sway motion matches the spatial parameters of the visual motion for a large range of frequencies. A linear dynamical model with constant parameters was compared quantitatively with the data. Its failure to describe correctly the spatiotemporal properties of the system led us to consider adaptive and nonlinear models. To differentiate between possible alternative structures we directly fitted nonlinear differential equations to the sway and visual motion trajectories on a trial-by-trial basis. We found that the eigenfrequency of the fitted model adapts strongly to the visual motion frequency. The damping coefficient decreases with increasing frequency. This indicates that the system destabilizes its postural state in the inertial frame. This leads to a faster internal dynamics which is capable of synchronizing posture with fast-moving visual environments. Using an algorithm which allows the identification of essentially nonlinear terms of the dynamics we found small nonlinear contributions. These nonlinearities are not consistent with a limit-cycle dynamics, accounting for the robustness of the amplitude of postural sway against frequency variations. We interpret out results in terms of active generation of postural sway specified by sensory information. We derive also a number of conclusions for a behavior-oriented analysis of the postural system.
站在正弦运动视觉环境中的人体受试者会表现出具有特征动力学特性的姿势摆动。在一项视觉运动频率变化的实验中，我们分析了这种摆动的时空特性。我们发现增益在1附近保持恒定，这意味着在大范围频率下，摆动运动与视觉运动的空间参数相匹配。将具有恒定参数的线性动力学模型与数据进行了定量比较。该模型未能正确描述系统的时空特性，这促使我们考虑自适应和非线性模型。为了区分可能的替代结构，我们在逐个试验的基础上直接将非线性微分方程拟合到摆动和视觉运动轨迹上。我们发现拟合模型的固有频率强烈适应视觉运动频率。阻尼系数随频率增加而减小。这表明系统在惯性系中使其姿势状态不稳定。这导致更快的内部动力学，能够使姿势与快速移动的视觉环境同步。使用一种能够识别动力学基本非线性项的算法，我们发现了小的非线性贡献。这些非线性与极限环动力学不一致，这解释了姿势摆动幅度对频率变化的鲁棒性。我们根据由感官信息指定的姿势摆动的主动生成来解释我们的结果。我们还得出了一些关于姿势系统行为导向分析的结论。
