The loss function of sensorimotor learning
The loss function of sensorimotor learning
复制标题
DOI：
10.1073/pnas.0308394101
复制
发表时间：
2004-06-29
影响因子：
11.1
通讯作者：
Wolpert, DM
中科院分区：
文献类型：
--
作者：
Körding, KP;Wolpert, DM
运动学习可以被定义为改变表现，以优化任务的某些功能，如准确性。被优化的准确性的度量被称为损失函数，并且指定CNS如何评定特定运动结果的相对成功或成本。在感觉运动控制和学习中的指向模型通常假设均方误差最小化的二次损失函数。在这里，我们开发了一种技术，用于测量与错误相关的损失。受试者被要求执行一项任务，而我们实验控制的偏度分布的错误，他们所经历的。根据受试者平均成绩的变化，我们推导出损失函数。我们表明，人们使用的损失函数中的成本增加约二次误差为小错误和显着小于二次为大错误。因此，该系统对离群值具有鲁棒性。这表明，感觉运动控制和学习的模型，假设最小化平方误差是一个很好的近似，但往往惩罚大的错误过度。
Motor learning can be defined as changing performance so as to optimize some function of the task, such as accuracy. The measure of accuracy that is optimized is called a loss function and specifies how the CNS rates the relative success or cost of a particular movement outcome. Models of pointing in sensorimotor control and learning usually assume a quadratic loss function in which the mean squared error is minimized. Here we develop a technique for measuring the loss associated with errors. Subjects were required to perform a task while we experimentally controlled the skewness of the distribution of errors they experienced. Based on the change in the subjects' average performance, we infer the loss function. We show that people use a loss function in which the cost increases approximately quadratically with error for small errors and significantly less than quadratically for large errors. The system is thus robust to outliers. This suggests that models of sensorimotor control and learning that have assumed minimizing squared error are a good approximation but tend to penalize large errors excessively.