A rational model of causal induction with continuous causes

Michael D. Pacer, Thomas L. Griffiths

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

5 Scopus citations

Abstract

Rational models of causal induction have been successful in accounting for people's judgments about causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2×2 contingency table. This severely limits the scope of these models, since the world often provides non-binary data. We develop a new rational model of causal induction using continuous dimensions, which aims to diminish the gap between empirical and theoretical approaches and real-world causal induction. This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people's inferences in a new experiment.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 24
Subtitle of host publication25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
StatePublished - Dec 1 2011
Externally publishedYes
Event25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 - Granada, Spain
Duration: Dec 12 2011Dec 14 2011

Other

Other25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
CountrySpain
CityGranada
Period12/12/1112/14/11

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Pacer, M. D., & Griffiths, T. L. (2011). A rational model of causal induction with continuous causes. In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011