Header logo is

Application of Monte Carlo Methods to Psychometric Function Fitting

2002

Poster

ei


The psychometric function relates an observer's performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. Here I describe methods to (1) fitting psychometric functions, (2) assessing goodness-of-fit, and (3) providing confidence intervals for the function's parameters and other estimates derived from them. First I describe a constrained maximum-likelihood method for parameter estimation. Using Monte-Carlo simulations I demonstrate that it is important to have a fitting method that takes stimulus-independent errors (or "lapses") into account. Second, a number of goodness-of-fit tests are introduced. Because psychophysical data sets are usually rather small I advocate the use of Monte Carlo resampling techniques that do not rely on asymptotic theory for goodness-of-fit assessment. Third, a parametric bootstrap is employed to estimate the variability of fitted parameters and derived quantities such as thresholds and slopes. I describe how the bootstrap bridging assumption, on which the validity of the procedure depends, can be tested without incurring too high a cost in computation time. Finally I describe how the methods can be extended to test hypotheses concerning the form and shape of several psychometric functions. Software describing the methods is available (http://www.bootstrap-software.com/psignifit/), as well as articles describing the methods in detail (Wichmann&Hill, Perception&Psychophysics, 2001a,b).

Author(s): Wichmann, FA.
Journal: Proceedings of the 33rd European Conference on Mathematical Psychology
Pages: 44
Year: 2002
Day: 0

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

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex

@poster{1896,
  title = {Application of Monte Carlo Methods to Psychometric Function Fitting},
  author = {Wichmann, FA.},
  journal = {Proceedings of the 33rd European Conference on Mathematical Psychology},
  pages = {44},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2002},
  doi = {}
}