@inproceedings{c6bc96d3d8dc4782863e4e9c247c632f,

title = "A Simple Sequential Algorithm for Approximating Bayesian Inference",

abstract = "People can apparently make surprisingly sophisticated inductive inferences, despite the fact that there are constraints on cognitive resources that would make performing exact Bayesian inference computationally intractable. What algorithms could they be using to make this possible? We show that a simple sequential algorithm, Win-Stay, Lose-Shift (WSLS), can be used to approximate Bayesian inference, and is consistent with human behavior on a causal learning task. This algorithm provides a new way to understand people{\textquoteright}s judgments and a new efficient method for performing Bayesian inference.",

keywords = "Bayesian inference, algorithmic level, causal learning",

author = "Elizabeth Bonawitz and Stephanie Denison and Annie Chen and Alison Gopnik and Griffiths, {Thomas L.}",

note = "Publisher Copyright: {\textcopyright} CogSci 2011.; 33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 ; Conference date: 20-07-2011 Through 23-07-2011",

year = "2011",

language = "English (US)",

series = "Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011",

publisher = "The Cognitive Science Society",

pages = "2463--2468",

editor = "Laura Carlson and Christoph Hoelscher and Shipley, {Thomas F.}",

booktitle = "Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011",

}