Modeling psychometric functions in R.
2006
Yssaad-Fesselier, Rosa | Knoblauch, Kenneth | Cerveau et vision ; Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)
We demonstrate some procedures in the statistical computing environment R for obtaining maximum likelihood estimates of the parameters of a psychometric function by fitting a generalized nonlinear regression model to the data. A feature for fitting a linear model to the threshold (or other) parameters of several psychometric functions simultaneously provides a powerful tool for testing hypotheses about the data and, potentially, for reducing the number of parameters necessary to describe them. Finally, we illustrate procedures for treating one parameter as a random effect that would permit a simplified approach to modeling stimulus-independent variability due to factors such as lapses or interobserver differences. These tools will facilitate a more comprehensive and explicit approach to the modeling of psychometric data.
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