期刊
COGNITIVE PSYCHOLOGY
卷 76, 期 -, 页码 1-29出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cogpsych.2014.11.001
关键词
Causal reasoning; Bayesian models; Computational modeling; Iterated learning
资金
- Air Force Office of Scientific Research [FA9550-13-1-0170, FA-9550-10-1-0232]
- McDonnell Causal Collaborative
- Basic Research Funds from Beijing Institute of Technology
- Cher Wang and Wenchi Chen of VIA Technologies
- National Natural Science Foundation of China [71373020]
- Beijing Program of Philosophy and Social Science [13JYB010]
- Learning science and Educational Development Laboratory at the Institute of Education, Beijing Institute of Technology
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. (C) 2014 Elsevier Inc. All rights reserved.
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