A Theory of Learning to Infer
A Theory of Learning to Infer
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DOI：
10.1037/rev0000178
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发表时间：
2020-04-01
影响因子：
5.4
通讯作者：
Gershman, Samuel J.
中科院分区：
文献类型：
--
作者：
Dasgupta, Ishita;Schulz, Eric;Gershman, Samuel J.
贝叶斯认知理论假设人们可以理性地整合概率。然而，一些实证研究结果与这一命题相矛盾：人类的概率推理倾向于系统偏离最优性。令人困惑的是，这些偏差有时会朝着相反的方向发展。一些研究表明，人们对先验概率的反应不足（基本率忽略），而另一些研究发现，人们对数据的可能性反应不足（保守主义）。我们认为，这些偏差的出现，因为人类的大脑并不完全依赖于一个通用的机制，近似贝叶斯推理是不变的查询。相反，大脑配备了一个识别模型，将查询映射到概率分布。该识别模型的参数被优化，以获得尽可能接近的输出，平均而言，真正的后验。由于我们有限的计算资源，识别模型将分配其资源，以便对高概率查询比低概率查询更准确。通过适应查询分布，识别模型学习推断。我们表明，这一理论可以解释为什么以及当人们反应不足的数据或先验，和一个新的实验表明，这两种形式的反应不足，可以系统地控制通过操纵查询分布。该理论还解释了一系列相关现象：记忆效应，信念偏差和概率推理中的反应变异性结构。我们还讨论了如何将理论与先前的抽样为基础的近似推理帐户。
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 (base rate neglect), other studies find that people underreact to the likelihood of the data (conservatism). 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 samplingbased accounts of approximate inference.