Modeling psychometric functions in R.

Rosa Yssaad-Fesselier 1 Kenneth Knoblauch 1, *
* Corresponding author
Abstract : 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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Journal articles
Behavior Research Methods, Psychonomic Society, Inc, 2006, 38 (1), pp.28-41
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Rosa Yssaad-Fesselier, Kenneth Knoblauch. Modeling psychometric functions in R.. Behavior Research Methods, Psychonomic Society, Inc, 2006, 38 (1), pp.28-41. 〈inserm-00131799〉

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