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  Bayesian inference for psychometric functions

Kuss, M., Jäkel, F., & Wichmann, F. (2005). Bayesian inference for psychometric functions. Journal of Vision, 5(5): 8, pp. 478-492. doi:10.1167/5.5.8.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D587-E Version Permalink: http://hdl.handle.net/21.11116/0000-0004-DC01-3
Genre: Journal Article

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 Creators:
Kuss, M1, 2, Author              
Jäkel, F1, 2, Author              
Wichmann, FA1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer’s ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.

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 Dates: 2005-05
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1167/5.5.8
BibTex Citekey: 3443
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Title: Journal of Vision
Source Genre: Journal
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Publ. Info: Charlottesville, VA : Scholar One, Inc.
Pages: - Volume / Issue: 5 (5) Sequence Number: 8 Start / End Page: 478 - 492 Identifier: ISSN: 1534-7362
CoNE: https://pure.mpg.de/cone/journals/resource/111061245811050