A Bayesian approach to diffusion models of decision-making and response time
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(Published version)
Date
2007
Authors
Lee, M.
Fuss, I.
Navarro, D.
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Conference paper
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Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference / B. Schölkopf, J. Platt, T. Hofmann (eds.): pp. 809-816
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Michael D. Lee, Ian G. Fuss, Danieal J. Navarro
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Annual Conference on Neural Information Processing Systems (20th : 2006 : Vancouver, Canada)
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.
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© 2007 Massachusetts Institute of Technology