categories, which subsequently serve as determinants for how a stimulus gets categorized. Examples of categorization models Prototype models Weighted Features Prototype Model An early instantiation of the prototype model was produced by Reed in the early 1970s. Reed (1972) conducted a series of experiments to compare the performance of 18 models on explaining data from a categorization task that required participants to sort faces into one of two categories. Results suggested that the prevailing model was the weighted features prototype model, which belonged to the family of average distance models. Unlike traditional average distance models, however, this model differentially weighted the most distinguishing features of the two categories. Given this model's performance, Reed (1972) concluded that the strategy participants used during the face categorization task was to construct prototype representations for each of the two categories of faces and categorize test patterns into the category associated with the most similar prototype. Furthermore, results suggested that similarity was determined by each categories most discriminating features. Exemplar models Generalized Context Model Medin and Schaffer's (1978) context model was expanded upon by Nosofsky (1986) in the mid-1980's, resulting in the production of the Generalized Context Model (GCM). The GCM is an exemplar model that stores exemplars of stimuli as exhaustive combinations of the features associated with each exemplar. By storing these combinations, the model establishes contexts for the features of each exemplar, which are defined by all other features with which that feature co-occurs. The GCM computes the similarity of an exemplar and a stimulus in two steps. First, the GCM computes the psychological distance between the exemplar and the stimulus. This is accomplished by summing the absolute values of the dimensional difference between the exemplar and the stimulus. For example, suppose an exemplar has a value of 18 on dimension X and the stimulus has a value of 42 on dimension X; the resulting dimensional difference would be 24. Once psychological distance has been evaluated, an exponential decay function determines the similarity of the exemplar and the stimulus, where a distance of 0 results in a similarity of 1 (which begins to decrease exponentially as distance increases). Categorical responses are then generated by evaluating the similarity of the stimulus to each category's exemplars, where each exemplar provides a "vote" to their respective categories that varies in strength based on the exemplar's similarity to the stimulus and the strength of the exemplar's association