@inproceedings{6916ccfea1864e968c10134bcbda5f43,
title = "Elements of a rational framework for continuous-time causal induction",
abstract = "Temporal information plays a major role in human causal inference. We present a rational framework for causal induction from events that take place in continuous time. We define a set of desiderata for such a framework and outline a strategy for satisfying these desiderata using continuous-time stochastic processes. We develop two specific models within this framework, illustrating how it can be used to capture both generative and preventative causal relationships as well as delays between cause and effect. We evaluate one model through a new behavioral experiment, and the other through a comparison to existing data.",
author = "Michael Pacer and Griffiths, {Thomas L.}",
note = "Publisher Copyright: {\textcopyright} CogSci 2012.All rights reserved.; 34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012 ; Conference date: 01-08-2012 Through 04-08-2012",
year = "2012",
language = "English (US)",
series = "Building Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012",
publisher = "The Cognitive Science Society",
pages = "833--838",
editor = "Naomi Miyake and David Peebles and Cooper, {Richard P.}",
booktitle = "Building Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012",
}