TY - JOUR
T1 - Identifying expectations about the strength of causal relationships
AU - Yeung, Saiwing
AU - Griffiths, Thomas L.
N1 - Funding Information:
A previous version of this work was presented at the 33rd Annual Conference of the Cognitive Science Society. We thank Doug Medin, Mike Oaksford, David Shanks, Tom Beckers, Michael Ranney, and three anonymous reviewers for helpful comments. We also thank Hongjing Lu for providing the code for the sparse and strong model, and data from the paper. This work was supported by grants FA9550-13-1-0170 and FA-9550-10-1-0232 from the Air Force Office of Scientific Research , a grant from the McDonnell Causal Collaborative , Basic Research Funds from Beijing Institute of Technology , a grant from Cher Wang and Wenchi Chen of VIA Technologies , National Natural Science Foundation of China (Program Code: 71373020), Beijing Program of Philosophy and Social Science (Program Code: 13JYB010), and the Learning Science and Educational Development Laboratory at the Institute of Education, Beijing Institute of Technology .
Publisher Copyright:
© 2014 Elsevier Inc.
PY - 2015/2/1
Y1 - 2015/2/1
N2 - When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people's a priori beliefs about causal systems, with recent research focusing on people's expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning-a method in which participants make inferences about data generated based on their own responses in previous trials-to estimate participants' prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants' prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.
AB - When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people's a priori beliefs about causal systems, with recent research focusing on people's expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning-a method in which participants make inferences about data generated based on their own responses in previous trials-to estimate participants' prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants' prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.
KW - Bayesian models
KW - Causal reasoning
KW - Computational modeling
KW - Iterated learning
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U2 - 10.1016/j.cogpsych.2014.11.001
DO - 10.1016/j.cogpsych.2014.11.001
M3 - Article
C2 - 25522277
AN - SCOPUS:84916885193
SN - 0010-0285
VL - 76
SP - 1
EP - 29
JO - Cognitive Psychology
JF - Cognitive Psychology
ER -