A Generative Model of Causal Cycles

Bob Rehder, Jay B. Martin

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

Abstract

Causal graphical models (CGMs) have become popular in numerous domains of psychological research for representing people’s causal knowledge. Unfortunately, however, the CGMs typically used in cognitive models prohibit representations of causal cycles. Building on work in machine learning, we propose an extension of CGMs that allows cycles and apply that representation to one real-world reasoning task, namely, classification. Our model’s predictions were assessed in experiments that tested both probabilistic and deterministic causal relations. The results were qualitatively consistent with the predictions of our model and inconsistent with those of an alternative model.

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
Pages2944-2949
Number of pages6
ISBN (Electronic)9780976831877
StatePublished - 2011
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

ASJC Scopus subject areas

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

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