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  Bayesian Inference for Psychometric Functions

Kuss, M., Jäkel, F., & Wichmann, F. (2005). Bayesian Inference for Psychometric Functions. Poster presented at 8th Tübinger Wahrnehmungskonferenz (TWK 2005), Tübingen, Germany.

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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 of perception the psychometric function is used to model the relation between the physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. We propose the use of Bayesian inference to extract the information contained in experimental data to learn about the parameters of psychometric functions. Since Bayesian inference cannot be performed analytically we use a Markov chain Monte Carlo method to generate samples from the posterior distribution over parameters. These samples can be used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. We compare our approach with traditional methods based on maximum-likelihood parameter estimation combined with parametric bootstrap techniques for confidence interval estimation. Experiments indicate that Bayesian inference methods are superior to bootstrap-based methods and are thus the method of choice for estimating the psychometric function and its confidence-intervals.

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 Dates: 2005-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3235
 Degree: -

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Title: 8th Tübinger Wahrnehmungskonferenz (TWK 2005)
Place of Event: Tübingen, Germany
Start-/End Date: 2005-02-25 - 2005-02-27

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Title: 8th Tübingen Perception Conference: TWK 2005
Source Genre: Proceedings
 Creator(s):
Bülthoff, HH1, Editor            
Mallot, HA, Editor            
Ulrich, R, Editor
Wichmann, FA1, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Kirchentellinsfurt, Germany : Knirsch
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 106 Identifier: ISBN: 3-927091-70-7