Blog
About

57
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      The psychometric function: I. Fitting, sampling, and goodness of fit

      ,

      Perception & Psychophysics

      Springer Science and Business Media LLC

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The psychometric function relates an observer's performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function's parameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximum-likelihood method of parameter estimation and developing several goodness-of-fit tests. Using Monte Carlo simulations, we deal with two specific difficulties that arise when fitting functions to psychophysical data. First, we note that human observers are prone to stimulus-independent errors (or lapses). We show that failure to account for this can lead to serious biases in estimates of the psychometric function's parameters and illustrate how the problem may be overcome. Second, we note that psychophysical data sets are usually rather small by the standards required by most of the commonly applied statistical tests. We demonstrate the potential errors of applying traditional chi2 methods to psychophysical data and advocate use of Monte Carlo resampling techniques that do not rely on asymptotic theory. We have made available the software to implement our methods.

          Related collections

          Most cited references 23

          • Record: found
          • Abstract: not found
          • Book: not found

          An Introduction to the Bootstrap

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Bootstrap Methods: Another Look at the Jackknife

             B Efron (1979)
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Generalized linear models (Second edition).

                Bookmark

                Author and article information

                Journal
                Perception & Psychophysics
                Perception & Psychophysics
                Springer Science and Business Media LLC
                0031-5117
                1532-5962
                November 2001
                November 2001
                : 63
                : 8
                : 1293-1313
                10.3758/BF03194544
                11800458
                © 2001

                http://www.springer.com/tdm

                Molecular medicine, Neurosciences

                Comments

                Comment on this article