Editing Cognitive categorization (section)

===Hybrid models===

'''SUSTAIN (Supervised and Unsupervised [[Stratified charge engine|Stratified]] Adaptive Incremental Network)<ref name="Love, B. C. 2004">{{Cite journal |last1=Love |first1=Bradley C. |last2=Medin |first2=Douglas L. |last3=Gureckis |first3=Todd M. |date=2004 |title=SUSTAIN: A Network Model of Category Learning. |journal=Psychological Review |language=en |volume=111 |issue=2 |pages=309–332 |doi=10.1037/0033-295X.111.2.309 |pmid=15065912 |issn=1939-1471}}</ref>''' It is often the case that learned category representations vary depending on the learner's goals,<ref>{{Cite journal |last=Barsalou |first=Lawrence W. |date=1985 |title=Ideals, central tendency, and frequency of instantiation as determinants of graded structure in categories.|journal=Journal of Experimental Psychology: Learning, Memory, and Cognition |language=en |volume=11 |issue=4 |pages=629–654 |doi=10.1037/0278-7393.11.1-4.629 |pmid=2932520 |issn=1939-1285}}</ref> as well as how categories are used during learning.<ref name=":5" /> Thus, some categorization researchers suggest that a proper model of categorization needs to be able to account for the variability present in the learner's goals, tasks, and strategies.<ref name="Love, B. C. 2004"/> This proposal was realized by Love and colleagues (2004) through the creation of SUSTAIN, a flexible clustering model capable of accommodating both simple and complex categorization problems through incremental adaptation to the specifics of problems.

In practice, the SUSTAIN model first converts a stimulus' perceptual information into features that are organized along a set of dimensions. The representational space that encompasses these dimensions is then distorted (e.g., stretched or shrunk) to reflect the importance of each feature based on inputs from an attentional mechanism. A set of clusters (specific instances grouped by similarity) associated with distinct categories then compete to respond to the stimulus, with the stimulus being subsequently assigned to the cluster whose representational space is closest to the stimulus'. The unknown stimulus dimension value (e.g., category label) is then predicted by the winning cluster, which, in turn, informs the categorization decision.

The flexibility of the SUSTAIN model is realized through its ability to employ both supervised and unsupervised learning at the cluster level. If SUSTAIN incorrectly predicts a stimulus as belonging to a particular cluster, corrective feedback (i.e., supervised learning) would signal sustain to recruit an additional cluster that represents the misclassified stimulus. Therefore, subsequent exposures to the stimulus (or a similar alternative) would be assigned to the correct cluster. SUSTAIN will also employ unsupervised learning to recruit an additional cluster if the similarity between the stimulus and the closest cluster does not exceed a threshold, as the model recognizes the weak predictive utility that would result from such a cluster assignment. SUSTAIN also exhibits flexibility in how it solves both simple and complex categorization problems. Outright, the internal representation of SUSTAIN contains only a single cluster, thus biasing the model towards simple solutions. As problems become increasingly complex (e.g., requiring solutions consisting of multiple stimulus dimensions), additional clusters are incrementally recruited so SUSTAIN can handle the rise in complexity.