Rosenbaum D A, Engelbrecht S E, Bushe M M, Loukopoulos L D
Dept. of Psychology, University of Massachusetts, Amherst 01003.
Acta Psychol (Amst). 1993 Mar;82(1-3):237-50. doi: 10.1016/0001-6918(93)90014-i.
In this paper we propose that reaches are made to target postures which are selected by evaluating stored postures. Target postures are chosen by taking a weighted average of the stored postures, where the weights assigned to the stored postures depend on their effectiveness for the task. Movements from starting postures to target postures are achieved by reducing the distance, in joint space, between the two. The form of the movement depends on drive (assumed to decrease as less distance remains) and inertia. The model predicts the Power Law of learning, compensation for immobility of joints, changes in limb contributions depending on movement speed, asymmetric bell-shaped velocity profiles, velocity-amplitude relations, Fitts' Law, and position-dependent variations in hand-path curvature. Planned extensions of the model may broaden its application-for example, to handwriting.
在本文中，我们提出，伸手动作是朝着通过评估存储姿势而选定的目标姿势进行的。目标姿势是通过对存储姿势取加权平均值来选择的，其中赋予存储姿势的权重取决于它们对任务的有效性。从起始姿势到目标姿势的动作是通过在关节空间中减小两者之间的距离来实现的。动作的形式取决于驱动力（假定随着剩余距离的减少而减小）和惯性。该模型预测了学习的幂定律、关节不动的补偿、取决于运动速度的肢体贡献变化、不对称钟形速度分布、速度-幅度关系、菲茨定律以及手部路径曲率的位置依赖性变化。该模型计划的扩展可能会拓宽其应用范围——例如，应用于手写。
