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 language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 24 |
Subtitle of host publication | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 |
State | Published - Dec 1 2011 |
Externally published | Yes |
Event | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 - Granada, Spain Duration: Dec 12 2011 → Dec 14 2011 |
Other
Other | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 |
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Country/Territory | Spain |
City | Granada |
Period | 12/12/11 → 12/14/11 |
All Science Journal Classification (ASJC) codes
- Information Systems