Dessing Joost C, Caljouw Simone R, Peper Peper E, Beek Peter J
Institute for Fundamental and Clinical Human Movement Sciences, Amsterdam/Nijmegen, The Netherlands.
Biol Cybern. 2004 Dec;91(6):377-87. doi: 10.1007/s00422-004-0520-4. Epub 2004 Nov 19.
Besides making contact with an approaching ball at the proper place and time, hitting requires control of the effector velocity at contact. A dynamical neural network for the planning of hitting movements was derived in order to account for both these requirements. The model in question implements continuous required velocity control by extending the Vector Integration To Endpoint model while providing explicit control of effector velocity at interception. It was shown that the planned movement trajectories generated by the model agreed qualitatively with the kinematics of hitting movements as observed in two recent experiments. Outstanding features of this comparison concerned the timing and amplitude of the empirical backswing movements, which were largely consistent with the predictions from the model. Several theoretical implications as well as the informational basis and possible neural underpinnings of the model were discussed.
除了在适当的时间和位置与飞来的球接触外，击球还需要在接触时控制效应器的速度。为了满足这两个要求，推导了一个用于击球动作规划的动态神经网络。该模型通过扩展“向量积分到终点”模型来实现连续的所需速度控制，同时在拦截时提供对效应器速度的明确控制。结果表明，该模型生成的计划运动轨迹在质量上与最近两个实验中观察到的击球动作运动学一致。这种比较的突出特点涉及经验性后摆动作的时间和幅度，这与模型的预测基本一致。讨论了该模型的几个理论意义以及信息基础和可能的神经基础。
