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Bayesian Inference for Psychometric Functions

Kuss, M. and Jäkel, F. and Wichmann, F. A. (2005):
Bayesian Inference for Psychometric Functions.
In: Journal of Vision, pp. 478-492, 5, DOI: 10.1167/5.5.8,
[Online-Edition: https://doi.org/10.1167/5.5.8],
[Article]

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.

Item Type: Article
Erschienen: 2005
Creators: Kuss, M. and Jäkel, F. and Wichmann, F. A.
Title: Bayesian Inference for Psychometric Functions
Language: English
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.

Journal or Publication Title: Journal of Vision
Volume: 5
Divisions: 03 Department of Human Sciences
03 Department of Human Sciences > Institute for Psychology
03 Department of Human Sciences > Institute for Psychology > Models of Higher Cognition
Date Deposited: 09 Jul 2018 09:04
DOI: 10.1167/5.5.8
Official URL: https://doi.org/10.1167/5.5.8
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