University of Warwick, Coventry, UK.
Warwick Business School, Coventry, UK.
Trends Cogn Sci. 2016 Dec;20(12):883-893. doi: 10.1016/j.tics.2016.10.003. Epub 2016 Oct 26.
Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy.
贝叶斯解释在过去的二十年中席卷了认知科学领域，从直观物理和因果学习，到感知、运动控制和语言。然而，人们甚至连最简单的概率问题都搞不清楚。这是什么原因呢？为什么据说具有贝叶斯思维的大脑在概率推理方面表现如此之差？在本文中，我们提出了一个直接的、或许出人意料的答案：贝叶斯大脑根本不需要表示或计算概率，而且实际上也不适合这样做。相反，大脑是一个贝叶斯抽样器。只有在无限样本的情况下，贝叶斯抽样器才符合概率规律；在有限样本的情况下，它会系统地产生经典的概率推理错误，包括解包效应、基数忽视和合取谬误。
