Editing Cognitive categorization (section)

===Exemplar models===

'''Generalized Context Model<ref name="Nosofsky, R. M. 1986">{{Cite journal |last=Nosofsky |first=Robert M. |date=1986 |title=Attention, similarity, and the identification–categorization relationship. |url=http://doi.apa.org/getdoi.cfm?doi=10.1037/0096-3445.115.1.39 |journal=Journal of Experimental Psychology: General |language=en |volume=115 |issue=1 |pages=39–57 |doi=10.1037/0096-3445.115.1.39 |pmid=2937873 |issn=1939-2222}}</ref>''' Medin and Schaffer's (1978) [[context model]] was expanded upon by Nosofsky (1986) in the mid-1980s, resulting in the production of the Generalized Context Model (GCM).<ref name="Nosofsky, R. M. 1986"/> The GCM is an exemplar model that stores exemplars of stimuli as exhaustive combinations of the features associated with each exemplar.<ref name="Kruschke, J. K. 2008"/> 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|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"<ref name="Kruschke, J. K. 2008"/> 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 with the category. This effectively assigns each category a selection probability that is determined by the proportion of votes it receives, which can then be fit to data.