We’re working to document and share our computational modelling code on Open Science Framework. One of the most widely used models is Rob Nosofsky’s Exemplar model known as the Generalized Context Model. This project can be found here, and is the result of Amanda Lien’s NSERC summer project in 2017. (Fun aside: I programmed a version of this model in 1998 using Turbo Pascal 7.0, later updated to REALBasic/Xojo, then a slow R script with loops, and finally with A. Lien’s help, an R version with matrices that minimizes running time. The underlying math is the same.)
This notebook describes the formulation of an exemplar model of categorization, the Generalized Context Model. Full specification of the model itself can be found elsewhere (Nosofsky 1984, Minda & Smith 2001) This model has been used in cognitive psychological research to make predictions about how participants will learn to classify objects and belonging to one or more category. The primary assumption of the exemplar model is that categories are represented in the mind by stored exemplar traces, rather that rules or prototypes.
The document describes the development and use of an R script that reads in a text file of classification probabilities (usually obtained from behavioural testing), a text file that corresponds to the stimuli in the experiment, and a text file that corresponds to the exemplars of each category. The model then uses a hill-climbing algorithm to adjust the parameters and minimize the fitting error. The model will report the best-fitting parameters, the fit index, and the prediction of the model.