A study of learning model of motor command and symbol for communication

关于平面上点对点的人手臂运动的特征，已经很好地证明了以下两个特征。已经提出了几种模型来解释这些特征。基于最优原则提出了四个准则。在该项目中，我们测量了点到点的运动在三维空间中，并进行了比较研究的最佳标准和测量的手臂姿势规划手臂姿势之间的定量。接下来，我们进行了比较检查之间的测量轨迹和最佳轨迹的基础上最小角度加加速度标准，最小转矩变化标准和最小命令转矩变化标准。最后，我们报告说，测量的手的轨迹和手臂的姿态是最接近的轨迹和姿态预测的最小命令的扭矩变化准则。人类可以使用复杂的运动进行通信。然而，所提出的模型的重点一直是从运动轨迹的表示和再生的运动表示。换句话说，这些模型不能模拟人类的表征获取过程，其中包括大量的尝试和错误。我们研究了一个复杂的运动模式和符号的需求过程。我们提出了两个模型，一个是一个模型，估计的运动，如通过点的随机表示，另一个是一个模型，其中估计表示相关的符号。第一个模型可以获得一组通过点，以再生一个原始图案顺利使用迭代过程。在学习实验中，我们试图通过点表征来获得运动模式。作为实验的结果，我们证实，我们的模型可以获得表示，使其能够再生的例子模式。我们证实了我们的第二个模型可以与符号和模式联系起来。这些模型可以为通信提供基本模型。

The following two characteristics have been well demonstrated about the feature of a point-to-point human arm movement on a plane. Several models have been proposed to explain the features. Four criteria based on optimal principles have been proposed. In the project, we measured point-to-point movements in three-dimensional space and performed comparative examinations quantitatively between arm posture planned by the optimal criterion and measuerd arm posture. Next, we performed comparative examinations between the measured trajectories and the optimal trajectories based on minimum angle jerk criterion, minimum torque change criterion and minimum commanded torque change criterion. Finally, we report that both of the measured hand trajectory and arm posture were closest to the trajectory and posture predicted by the minimum commanded torque change criterion.Humans can communicate using complex movements. However, the focus of the proposed models has been on transformation from movement trajectories to representations and on the regeneration of movements from representations. In other words, the models can not emulate the human representation acquirement process which includes lot of trial and error. We study a requirement process for complex movement patterns and symbols. We propose two models, one is a model that estimates the stochastic representation of a movement such as via-points, and the other is a model in which an estimated representation is related to a symbol. The first proposed model can acquire a set of via-points to regenerate an original pattern smoothly using on iterative process. In learning experiments, we tried to acquire movement patterns in via-point representation. As the result of experiments, we confirmed that our model can acquire representation that enables it to regenerate the example patterns. We confirmed that our second model can connect with symbols and patterns. The models can provide a fundamental model for communication.