Editing Cognitive categorization (section)

== Category learning ==
{{main|Category learning}}

''While an exhaustive discussion of category learning is beyond the scope of this article, a brief overview of category learning and its associated theories is useful in understanding formal models of categorization.''

If categorization research investigates how categories are maintained and used, the field of category learning seeks to understand how categories are acquired in the first place. To accomplish this, researchers often employ novel categories of arbitrary objects (e.g., dot matrices) to ensure that participants are entirely unfamiliar with the stimuli.<ref name="Ashby, F. G. 2005">{{Cite journal |last1=Ashby |first1=F. Gregory |last2=Maddox |first2=W. Todd |date=2005-02-01 |title=Human Category Learning |url=https://www.annualreviews.org/doi/10.1146/annurev.psych.56.091103.070217 |journal=Annual Review of Psychology |language=en |volume=56 |issue=1 |pages=149–178 |doi=10.1146/annurev.psych.56.091103.070217 |pmid=15709932 |issn=0066-4308|url-access=subscription }}</ref> Category learning researchers have generally focused on two distinct forms of category learning. [[Classification|Classification learning]] tasks participants with predicting category labels for a stimulus based on its provided features. Classification learning is centered around learning between-category information and the diagnostic features of categories.<ref name="Higgins, E. 2011">Higgins, E., & Ross, B. (2011). Comparisons in category learning: How best to compare for what. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 33, No. 33).</ref> In contrast, [[Inference|inference learning]] tasks participants with inferring the presence/value of a category feature based on a provided category label and/or the presence of other category features. Inference learning is centered on learning within-category information and the category's prototypical features.<ref name="Higgins, E. 2011"/>

Category learning tasks can generally be divided into two categories, supervised and unsupervised learning. [[Supervised learning]] tasks provide learners with category labels. Learners then use information extracted from labeled example categories to classify stimuli into the appropriate category, which may involve the [[abstraction]] of a rule or concept relating observed object features to category labels. [[Unsupervised learning]] tasks do not provide learners with category labels. Learners must therefore recognize inherent structures in a data set and group stimuli together by similarity into classes. Unsupervised learning is thus a process of generating a classification structure. Tasks used to study category learning take various forms:
* '''Rule-based tasks''' present categories that participants can learn through explicit reasoning processes. In these kinds of tasks, classification of stimuli is accomplished via the use of an acquired rule (i.e., if stimulus is large on dimension x, respond A).
* '''Information-integration tasks''' require learners to synthesize perceptual information from multiple stimulus dimensions prior to making categorization decisions. Unlike rule-based tasks, information-integration tasks do not afford rules that are easily articulable. Reading an X-ray and trying to determine if a tumor is present can be thought of as a real-world instantiation of an information-integration task.
* '''Prototype distortion tasks''' require learners to generate a prototype for a category. Candidate exemplars for the category are then produced by randomly manipulating the features of the prototype, which learners must classify as either belonging to the category or not.

===Category learning theories===

Category learning researchers have proposed various theories for how humans learn categories.<ref>{{Citation |last1=Ashby |first1=F. Gregory |title=Chapter 4 - Stimulus Categorization |date=1998-01-01 |url=https://www.sciencedirect.com/science/article/pii/B9780120999750500063 |work=Measurement, Judgment and Decision Making |pages=251–301 |editor-last=Birnbaum |editor-first=Michael H. |series=Handbook of Perception and Cognition (Second Edition) |place=San Diego |publisher=Academic Press |doi=10.1016/b978-012099975-0.50006-3 |isbn=978-0-12-099975-0 |last2=Maddox |first2=W. Todd|url-access=subscription }}</ref> Prevailing theories of category learning include the prototype theory, the exemplar theory, and the decision bound theory.<ref name="Ashby, F. G. 2005"/>

The prototype theory suggests that to learn a category, one must learn the category's prototype. Subsequent categorization of novel stimuli is then accomplished by selecting the category with the most similar prototype.<ref name="Ashby, F. G. 2005"/>

The exemplar theory suggests that to learn a category, one must learn about the exemplars that belong to that category. Subsequent categorization of a novel stimulus is then accomplished by computing its similarity to the known exemplars of potentially relevant categories and selecting the category that contains the most similar exemplars.<ref name="Medin, D. L. 1978"/>

Decision bound theory suggests that to learn a category, one must either learn the regions of a stimulus space associated with particular responses or the boundaries (the decision bounds) that divide these response regions. Categorization of a novel stimulus is then accomplished by determining which response region it is contained within.<ref>{{Cite journal |last1=Maddox |first1=W. Todd |last2=Ashby |first2=F. Gregory |date=1996 |title=Perceptual separability, decisional separability, and the identification–speeded classification relationship. |url=http://doi.apa.org/getdoi.cfm?doi=10.1037/0096-1523.22.4.795 |journal=Journal of Experimental Psychology: Human Perception and Performance |language=en |volume=22 |issue=4 |pages=795–817 |doi=10.1037/0096-1523.22.4.795 |pmid=8756953 |issn=1939-1277|url-access=subscription }}</ref>