The Psychometric Function: Continuation

  • Kenneth Knoblauch
  • Laurence T. Maloney
Part of the Use R! book series (USE R, volume 32)


In the previous chapter we showed how to use direct optimization methods and the generalized linear model (GLM) to fit psychometric functions to Yes–No data. In an extended example using GLM, we illustrated the procedure for selecting a model to fit multiple psychometric functions, across a series of experimental conditions and how to evaluate the goodness of fit of the model. In this chapter, we continue the exploration of fitting psychometric functions and demonstrate methods required when the observer’s task is to select one among many alternatives, a type of experiment referred to as m-alternative forced choice (mAFC). In addition, we illustrate methods for assigning standard errors and confidence limits to estimated parameters. Finally, we end with a short discussion of non- (or semi-)parametric methods for fitting psychometric functions.


Bootstrap Sample Psychometric Function Stimulus Level Linear Predictor Difference Limen 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Kenneth Knoblauch
    • 1
  • Laurence T. Maloney
    • 2
  1. 1.Department of Integrative NeurosciencesStem-cell and Brain Research Institute INSERM U846BronFrance
  2. 2.Department of Psychology Center for Neural ScienceNew York UniversityNew YorkUSA

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