A Simple Sequential Algorithm for Approximating Bayesian Inference

Elizabeth Bonawitz, Stephanie Denison, Annie Chen, Alison Gopnik, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

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’s judgments and a new efficient method for performing Bayesian inference.

Original languageEnglish (US)
Title of host publicationExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
EditorsLaura Carlson, Christoph Hoelscher, Thomas F. Shipley
PublisherThe Cognitive Science Society
Pages2463-2468
Number of pages6
ISBN (Electronic)9780976831877
StatePublished - 2011
Externally publishedYes
Event33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 - Boston, United States
Duration: Jul 20 2011Jul 23 2011

Publication series

NameExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Conference

Conference33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011
Country/TerritoryUnited States
CityBoston
Period7/20/117/23/11

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • algorithmic level
  • Bayesian inference
  • causal learning

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