Department of Physics, Harvard University.
Department of Psychology, Harvard University.
Psychol Rev. 2020 Apr;127(3):412-441. doi: 10.1037/rev0000178.
Bayesian theories of cognition assume that people can integrate probabilities rationally. However, several empirical findings contradict this proposition: human probabilistic inferences are prone to systematic deviations from optimality. Puzzlingly, these deviations sometimes go in opposite directions. Whereas some studies suggest that people underreact to prior probabilities (), other studies find that people underreact to the likelihood of the data (). We argue that these deviations arise because the human brain does not rely solely on a general-purpose mechanism for approximating Bayesian inference that is invariant across queries. Instead, the brain is equipped with a recognition model that maps queries to probability distributions. The parameters of this recognition model are optimized to get the output as close as possible, on average, to the true posterior. Because of our limited computational resources, the recognition model will allocate its resources so as to be more accurate for high probability queries than for low probability queries. By adapting to the query distribution, the recognition model learns to infer. We show that this theory can explain why and when people underreact to the data or the prior, and a new experiment demonstrates that these two forms of underreaction can be systematically controlled by manipulating the query distribution. The theory also explains a range of related phenomena: memory effects, belief bias, and the structure of response variability in probabilistic reasoning. We also discuss how the theory can be integrated with prior sampling-based accounts of approximate inference. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
贝叶斯认知理论假设人们能够理性地整合概率。然而，一些经验发现与这一主张相矛盾：人类的概率推断容易受到系统性偏离最优的影响。令人困惑的是，这些偏差有时会朝着相反的方向发展。虽然有些研究表明人们对先验概率反应不足（），但其他研究发现人们对数据的可能性反应不足（）。我们认为，这些偏差的出现是因为人类大脑并非仅依赖于一种通用的机制来近似贝叶斯推断，这种机制在不同的查询中是不变的。相反，大脑配备了一种识别模型，将查询映射到概率分布。这个识别模型的参数经过优化，可以使输出平均尽可能接近真实的后验。由于我们的计算资源有限，识别模型将分配其资源，以便在高概率查询中比在低概率查询中更准确。通过适应查询分布，识别模型学会了推断。我们表明，这个理论可以解释为什么以及何时人们对数据或先验反应不足，并且一个新的实验表明，这两种形式的反应不足可以通过操纵查询分布来系统地控制。该理论还解释了一系列相关现象：记忆效应、信念偏差以及概率推理中反应变异性的结构。我们还讨论了该理论如何与基于先验抽样的近似推理理论相整合。（PsycInfo 数据库记录（c）2020 APA，保留所有权利）。
