A Bayesian Model of Rule Induction in Raven’s Progressive Matrices

Daniel R. Little, Stephan Lewandowsky, Thomas L. Griffiths

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

16 Scopus citations

Abstract

Raven’s Progressive Matrices (Raven, Raven, & Court, 1998) is one of the most prevalent assays of fluid intelligence; however, most theoretical accounts of Raven’s focus on producing models which can generate the correct answer but do not fit human performance data. We provide a computational-level theory which interprets rule induction in Raven’s as Bayesian inference. The model computes the posterior probability of each rule in the set of possible rule hypotheses based on whether those rules could have generated the features of the objects in the matrix and the prior probability of each rule. Based on fits to both correct and incorrect response options across both the Standard and Advanced Progressive Matrices, we propose several novel mechanisms that may drive responding to Raven’s items.

Original languageEnglish (US)
Title of host publicationBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012
EditorsNaomi Miyake, David Peebles, Richard P. Cooper
PublisherThe Cognitive Science Society
Pages1918-1923
Number of pages6
ISBN (Electronic)9780976831884
StatePublished - 2012
Externally publishedYes
Event34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012 - Sapporo, Japan
Duration: Aug 1 2012Aug 4 2012

Publication series

NameBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012

Conference

Conference34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012
Country/TerritoryJapan
CitySapporo
Period8/1/128/4/12

All Science Journal Classification (ASJC) codes

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

Keywords

  • Bayesian inference
  • Raven’s Progressive Matrices
  • Rule induction

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