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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >The loss function of sensorimotor learning.
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The loss function of sensorimotor learning.

机译：感觉运动学习的丧失功能。

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摘要

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.
机译：运动学习可以定义为改变性能，以优化任务的某些功能，例如准确性。优化的精度度量称为损失函数，它指定CNS如何评估特定运动结果的相对成功或成本。感觉运动控制和学习中的指向模型通常采用二次损失函数，其中均方误差最小。在这里，我们开发了一种用于测量与错误相关的损耗的技术。在我们通过实验控制他们所经历的错误分布的偏度的同时，要求受试者执行一项任务。根据受试者平均成绩的变化，我们推导损失函数。我们证明人们使用损失函数，其中对于小错误，成本增加大约两倍，且有误差，而对于大错误，成本明显少于增加。因此，该系统对异常值具有鲁棒性。这表明假设最小化平方误差的感觉运动控制和学习模型是一个很好的近似值，但往往会严重惩罚较大的误差。

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