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Bayesian Inference for Psychometric Functions




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.

Author(s): Kuss, M. and Jäkel, F. and Wichmann, FA.
Volume: 8
Pages: 106
Year: 2005
Month: February
Day: 0
Editors: B{\"u}lthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
Event Name: 8th T{\"u}bingen Perception Conference (TWK 2005)
Event Place: T{\"u}bingen, Germany
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Bayesian Inference for Psychometric Functions},
  author = {Kuss, M. and J{\"a}kel, F. and Wichmann, FA.},
  volume = {8},
  pages = {106},
  editors = {B{\"u}lthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2005},
  month_numeric = {2}