Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference
Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference
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DOI：
10.1016/j.cogpsych.2014.06.003
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发表时间：
2014-11-01
影响因子：
2.6
通讯作者：
Griffiths, Thomas L.
中科院分区：
文献类型：
--
作者：
Bonawitz, Elizabeth;Denison, Stephanie;Griffiths, Thomas L.
人们可以以与贝叶斯认知模型一致的方式行事，尽管执行精确的贝叶斯推理在计算上具有挑战性。人们可以使用什么算法来实现这一点？我们表明，一个简单的顺序算法“赢留，损失样本”，受赢留，损失转移（WSLS）原则的启发，可以用来近似贝叶斯推理。我们调查了成年人和学龄前儿童在两个因果学习任务中的行为，以测试人们是否会使用类似的算法。这些研究使用了一种“微型微观遗传学方法”，调查人们在遇到新证据时如何顺序更新他们的信念。实验1研究了确定性因果学习场景，实验2和3研究了人们在随机场景中如何进行推理。在这些实验中，成年人和学龄前儿童的行为与我们的贝叶斯版本的WSLS原则是一致的。该算法提供了一个实用的方法来执行贝叶斯推理和一个新的方式来理解人们的判断。(C)2014爱思唯尔公司All rights reserved.
People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a "mini-microgenetic method", investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments. (C) 2014 Elsevier Inc. All rights reserved.