@inproceedings{b155764ad700485995cc9465ff476b05,
title = "Estimating human priors on causal strength",
abstract = "Bayesian models of human causal induction rely on assumptions about people{\textquoteright}s priors that have not been extensively tested. We empirically estimated human priors on the strength of causal relationships using iterated learning, an experimental method where people make inferences from data generated based on their own responses in previous trials. This method produced a prior on causal strength that was quite different from priors previously proposed in the literature on causal induction. The predictions of Bayesian models using different priors were then compared against human judgments of strength of causal relationships. The empirical priors estimated via iterated learning resulted in the best predictions.",
keywords = "Bayesian inference, Causal learning, Iterated learning, Probabilistic judgment",
author = "Saiwing Yeung and Griffiths, {Thomas L.}",
note = "Publisher Copyright: {\textcopyright} CogSci 2011.; 33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 ; Conference date: 20-07-2011 Through 23-07-2011",
year = "2011",
language = "English (US)",
series = "Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011",
publisher = "The Cognitive Science Society",
pages = "1709--1714",
editor = "Laura Carlson and Christoph Hoelscher and Shipley, {Thomas F.}",
booktitle = "Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011",
}