Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/68612
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 Responsibility: | 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 |
Description (link): | http://nips.cc/Conferences/2006/ |
Published version: | http://papers.nips.cc/paper/3036-a-bayesian-approach-to-diffusion-models-of-decision-making-and-response-time |
Appears in Collections: | Aurora harvest 2 Environment Institute publications Psychology publications |
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File | Description | Size | Format | |
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hdl_68612.pdf | Published version | 198.72 kB | Adobe PDF | View/Open |
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