Editing Cognitive categorization (section)

== Formal models ==

[[Computational models]] of categorization have been developed to test theories about how humans represent and use category information.<ref name="Kruschke, J. K. 2008"/> To accomplish this, categorization models can be fit to experimental data to see how well the predictions afforded by the model line up with human performance. Based on the model's success at explaining the data, theorists are able to draw conclusions about the accuracy of their theories and their theory's relevance to human category representations.

To effectively capture how humans represent and use category information, categorization models generally operate under variations of the same three basic assumptions.<ref name="Ashby, F. G. 1993">{{Cite journal |last1=Ashby |first1=F. Gregory |last2=Maddox |first2=W. Todd |date=1993-09-01 |title=Relations between Prototype, Exemplar, and Decision Bound Models of Categorization |url=https://www.sciencedirect.com/science/article/pii/S0022249683710230 |journal=Journal of Mathematical Psychology |language=en |volume=37 |issue=3 |pages=372–400 |doi=10.1006/jmps.1993.1023 |issn=0022-2496}}</ref><ref name="Kruschke, J. K. 2008"/> First, the model must make some kind of assumption about the internal representation of the stimulus (e.g., representing the perception of a stimulus as a point in a multi-dimensional space).<ref name="Ashby, F. G. 1993"/> Second, the model must make an assumption about the specific information that needs to be accessed in order to formulate a response (e.g., exemplar models require the collection of all available exemplars for each category).<ref name="Medin, D. L. 1978"/> Third, the model must make an assumption about how a response is selected given the available information.<ref name="Ashby, F. G. 1993"/>

Though all categorization models make these three assumptions, they distinguish themselves by the ways in which they represent and transform an input into a response representation.<ref name="Kruschke, J. K. 2008"/> The internal knowledge structures of various categorization models reflect the specific representation(s) they use to perform these transformations. Typical representations employed by models include exemplars, prototypes, and rules.<ref name="Kruschke, J. K. 2008"/>
* '''Exemplar models''' store all distinct instances of stimuli with their corresponding category labels in memory. Categorization of subsequent stimuli is determined by the stimulus' collective similarity to all known exemplars.
* '''Prototype models''' store a summary representation of all instances in a category. Categorization of subsequent stimuli is determined by selecting the category whose prototype is most similar to the stimulus.
* '''Rule-based models''' define categories by storing summary lists of the necessary and sufficient features required for category membership. Boundary models can be considered as atypical rule models, as they do not define categories based on their content. Rather, boundary models define the edges (boundaries) between categories, which subsequently serve as determinants for how a stimulus gets categorized.