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Type: Conference paper
Title: A Bayesian approach to diffusion models of decision-making and response time
Author: Lee, M.
Fuss, I.
Navarro, D.
Citation: Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference / B. Schölkopf, J. Platt, T. Hofmann (eds.): pp. 809-816
Publisher: MIT Press
Publisher Place: Cambridge, Mass.
Issue Date: 2007
ISBN: 9780262195683
ISSN: 1049-5258
Conference Name: Annual Conference on Neural Information Processing Systems (20th : 2006 : Vancouver, Canada)
Statement of
Michael D. Lee, Ian G. Fuss, Danieal J. Navarro
Abstract: We present a computational Bayesian approach for Wiener diffusion models, which are prominent accounts of response time distributions in decision-making. We first develop a general closed-form analytic approximation to the response time distributions for one-dimensional diffusion processes, and derive the required Wiener diffusion as a special case. We use this result to undertake Bayesian modeling of benchmark data, using posterior sampling to draw inferences about the interesting psychological parameters. With the aid of the benchmark data, we show the Bayesian account has several advantages, including dealing naturally with the parameter variation needed to account for some key features of the data, and providing quantitative measures to guide decisions about model construction.
Rights: © 2007 Massachusetts Institute of Technology
RMID: 0020113747
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Appears in Collections:Psychology publications
Environment Institute publications

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