Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent

Thomas L. Griffiths, Joshua B. Tenenbaum

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should combine prior knowledge with observed data. Comparing this model with human judgments provides constraints on possible algorithms that people might use to predict the future. In the experiments, we examine the effects of multiple observations, the effects of prior knowledge, and the difference between independent and dependent observations, using both descriptions and direct experience of prediction problems. The results indicate that people integrate prior knowledge and observed data in a way that is consistent with our Bayesian model, ruling out some simple heuristics for predicting the future. We suggest some mechanisms that might lead to more complete algorithmic-level accounts.

Original languageEnglish (US)
Pages (from-to)725-743
Number of pages19
JournalJournal of Experimental Psychology: General
Volume140
Issue number4
DOIs
StatePublished - Nov 1 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Psychology(all)
  • Developmental Neuroscience

Keywords

  • Bayesian inference
  • Heuristics
  • Mathematical modeling
  • Predicting the future

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