Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference

Elizabeth Bonawitz

doi:10.1016/j.cogpsych.2014.06.003

Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference

2014-11-01

102

OA

AI

DOI：10.1016/j.cogpsych.2014.06.003

原文链接

查看原文

摘要

En 中文

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.

Keyword:

Bayesian inference

Algorithmic level

Causal learning

Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference

摘要

AI总结

期刊

机构

引用论文