B6: Statistics of Signal Detection Models K Knoblauch |
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Inserm 0846 Stem-Cell and Brain Research Institute Bron, France |
| This tutorial will focus on the statistical tools to analyze and to model psychophysical experiments within the framework of Signal Detection Theory. This includes choice experiments (detection, discrimination, identification, etc.) and rating scale experiments with ROC analyses. In many cases, the decision rule underlying these paradigms is linear, thereby permitting the analyses to be simplified to a Generalized Linear Model (GLM). Rating scales, similarly, are analyzed by using ordinal regression models with cumulative link functions. With these approaches, we can define straight-forward procedures to fit the data, to test hypotheses about them, to obtain confidence intervals, etc. Diagnostic plots and tests will be used to evaluate goodness of fit and to explain some potential pitfalls that can occur in the data. Most off-the-shelf software packages now include tools for performing GLMs, thus, making it easy to implement these tests and procedures. Examples will be shown using the R programming environment and language (http://www.r-project.org/). Extensions of these models to include random effects allow estimation and control for observer and stimulus variability. Finally, an example will be shown of this approach with a paradigm for measuring appearance. Background reading includes the recent books "Modeling Psychophysical Data in R", K. Knoblauch & L. T. Maloney, 2012, Springer, (for R users) and "Psychophysics: A Practical Introduction", F. A. A. Kingdom & N. Prins, 2010, Academic Press (for matlab users). |
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