论文标题
随时预测作为人类反应时间的模型
Anytime Prediction as a Model of Human Reaction Time
论文作者
论文摘要
如今，神经网络通常像人们一样识别对象，因此可能是人类认可过程的模型。但是，大多数此类网络在固定的计算工作之后提供答案，而人类反应时间则有所不同，例如从0.2到10 s，具体取决于刺激和任务的特性。为了建模难度对人类反应时间的影响，我们考虑了一个分类网络，该网络使用早期外观分类器来做出任何时间预测。比较在添加高斯噪声中对CIFAR-10图像进行分类中的人和MSDNET精度，我们发现网络等效输入噪声SD比人高15倍，并且人类效率仅为网络的0.6 \％。当存在适当量的噪声以使两个观察者（人类和网络）进入相同的精度范围时，它们对持续时间或失败的依赖非常相似，即非常相似的速度准确性折衷。我们得出的结论是，任何时间分类（即早期退出）是识别任务中人类反应时间的有前途的模型。
Neural networks today often recognize objects as well as people do, and thus might serve as models of the human recognition process. However, most such networks provide their answer after a fixed computational effort, whereas human reaction time varies, e.g. from 0.2 to 10 s, depending on the properties of stimulus and task. To model the effect of difficulty on human reaction time, we considered a classification network that uses early-exit classifiers to make anytime predictions. Comparing human and MSDNet accuracy in classifying CIFAR-10 images in added Gaussian noise, we find that the network equivalent input noise SD is 15 times higher than human, and that human efficiency is only 0.6\% that of the network. When appropriate amounts of noise are present to bring the two observers (human and network) into the same accuracy range, they show very similar dependence on duration or FLOPS, i.e. very similar speed-accuracy tradeoff. We conclude that Anytime classification (i.e. early exits) is a promising model for human reaction time in recognition tasks.
