@inproceedings{8b344c8f92614793b827936a18d1236c,
title = "Dynamic Causal Learning",
abstract = "Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people's short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.",
author = "David Danks and Griffiths, {Thomas L.} and Tenenbaum, {Joshua B.}",
note = "Publisher Copyright: {\textcopyright} NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.; 15th International Conference on Neural Information Processing Systems, NIPS 2002 ; Conference date: 09-12-2002 Through 14-12-2002",
year = "2002",
language = "English (US)",
series = "NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems",
publisher = "MIT Press Journals",
pages = "67--74",
editor = "Suzanna Becker and Sebastian Thrun and Klaus Obermayer",
booktitle = "NIPS 2002",
address = "United States",
}