@inproceedings{bd2e2f7f61cb40789a62aa542206f25e,
title = "Upsetting the contingency table: Causal induction over sequences of point events",
abstract = "Data continuously stream into our minds, guiding our learning and inference with no trial delimiters to parse our experience. These data can take on a variety of forms, but research on causal learning has emphasized discrete contingency data over continuous sequences of events. We present a formal framework for modeling causal inferences about sequences of point events, based on Bayesian inference over nonhomogeneous Poisson processes (NHPPs). We show how to apply this framework to successfully model data from an experiment by Lagnado and Speekenbrink (2010) which examined human learning from sequences of point events.",
keywords = "Bayesian models, causal inference, continuous time, stochastic processes",
author = "Pacer, {Michael D.} and Griffiths, {Thomas L.}",
note = "Publisher Copyright: {\textcopyright} Cognitive Science Society, CogSci 2015.All rights reserved.; 37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015 ; Conference date: 23-07-2015 Through 25-07-2015",
year = "2015",
language = "English (US)",
series = "Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015",
publisher = "The Cognitive Science Society",
pages = "1805--1810",
editor = "Noelle, {David C.} and Rick Dale and Anne Warlaumont and Jeff Yoshimi and Teenie Matlock and Jennings, {Carolyn D.} and Maglio, {Paul P.}",
booktitle = "Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015",
}