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. Rule-based models RULEX (Rule-Plus-Exception) Model While simple logical rules are ineffective at learning poorly defined category structures, some proponents of the rule-based theory of categorization suggest that an imperfect rule can be used to learn such category structures if exceptions to that rule are also stored and considered. To formalize this proposal, Nosofsky and colleagues (1994) designed the RULEX model. The RULEX model attempts to form a decision tree composed of sequential tests of an object's attribute values. Categorization of the object is then determined by the outcome of these sequential tests. The RULEX model searches for rules in the following ways: Exact Search for a rule that uses a single attribute to discriminate between classes without error. Imperfect Search for a rule that uses a single attribute to discriminate between classes with few errors Conjunctive Search for a rule that uses multiple attributes to discriminate between classes with few errors. Exception Search for exceptions to the rule. The method that RULEX uses to perform these searches is as follows: First, RULEX attempts an exact search. If successful, then RULEX will continuously apply that rule until misclassification occurs. If the exact search fails to identify a rule, either an imperfect or conjunctive search will begin. A sufficient, though imperfect, rule acquired during one of these search phases will become permanently implemented and the RULEX model will then begin to search for exceptions. If no rule is acquired, then the model will attempt the search it did not perform in the previous phase. If successful, RULEX will permanently implement the rule and then begin an exception search. If none of the previous search methods are successful RULEX will default to only searching for exceptions, despite lacking an associated rule, which equates to acquiring a random rule. Hybrid models SUSTAIN (Supervised and Unsupervised Stratified Adaptive Incremental Network) It is often the case that learned category representations vary depending on the learner's goals, as well as how categories are used during learning. 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. This proposal was realized by Love and colleagues (2004) through the creation of SUSTAIN, a