The controversy over multiple category-learning systems is reminiscent of the controversy over multiple memory systems. Researchers continue to seek paradigms to sharply dissociate explicit category-learning processes (featuring verbalizeable category rules) from implicit category-learning processes (featuring learned stimulus-response associations that lie outside of declarative cognition). We contribute a new dissociative paradigm, adapting from comparative psychology the technique of deferred-rearranged reinforcement. Participants learned matched category tasks that had either a one-dimensional, rule-based solution or a multidimensional, information-integration solution. They received feedback only after each block of trials, with their positive outcomes grouped and their negative outcomes grouped. Deferred-rearranged reinforcement qualitatively eliminated implicit, information-integration category learning. It left intact explicit, rule-based category learning. Moreover, implicit category learners—facing deferred-rearranged reinforcement—turned by default and information-processing necessity to rule-based strategies that poorly suited their nominal category task. The results represent one of the strongest explicit-implicit dissociations yet seen in the categorization literature.

There is evidence that rule-based category learning is supported by a broad neural network that includes the prefrontal cortex, the anterior cingulate cortex, the head of the caudate nucleus, and medial temporal lobe structures. Although thousands of studies have examined rule-based category learning, only a few have studied the development of automaticity in rule-based tasks. Categorizing by a newly learned rule makes heavy demands on declarative memory, but after thousands of repetitions rule-based categorizations are made with no apparent effort. Thus, it seems likely that the neural systems that mediate automatic rule-based categorization are substantially different from the systems that mediate initial learning. This research aims at identifying the neural systems responsible for early and late rule-based categorization performances. Toward this end, this article reports the results of an experiment in which human participants each practiced a rule-based categorization task for Ͼ10,000 trials distributed over 20 separate sessions. Sessions 1, 4, 10, and 20 were performed inside a magnetic resonance imaging scanner. The main findings are as follows: (1) cortical activation remained approximately constant throughout training, (2) subcortical activation increased with practice (i.e., there were more activated voxels in the striatum), and (3) only cortical activation was correlated with accuracy after extensive training. The results suggest an initial subcortical neural system centered around the head of the caudate that is gradually replaced by a cortical system centered around the ventrolateral prefrontal cortex. With extensive practice, the cortical system progressively becomes more caudal and dorsal, and is eventually centered around the premotor cortex.

Analogical transfer is the ability to transfer knowledge despite significant changes in the surface features of a problem. In categorization, analogical transfer occurs if a classification strategy learned with one set of stimuli can be transferred to a set of novel, perceptually distinct stimuli. Three experiments investigated analogical transfer in rule-based and information-integration categorization tasks. In rule-based tasks, the optimal strategy is easy to describe verbally, whereas in information-integration tasks, accuracy is maximized only if information from two or more stimulus dimensions is integrated in a way that is difficult or impossible to describe verbally. In all three experiments, analogical transfer was nearly perfect in the rule-based conditions, but no evidence for analogical transfer was found in the information-integration conditions. These results were predicted a priori by the COVIS theory of categorization.

Considerable evidence suggests that human category learning recruits multiple memory systems. A popular assumption is that procedural memory is used to form stimulus-to-response mappings, whereas declarative memory is used to form and test explicit rules about category membership. The multiple systems framework has been successful in motivating and accounting for a broad array of empirical observations over the last 20 years. Even so, only a couple of studies have examined how the different categorization systems interact. Both previous studies suggest that switching between explicit and procedural responding is extremely difficult. But they leave unanswered the critical questions of whether trial-by-trial system switching is possible, and if so, whether it is qualitatively different than trial-by-trial switching between two explicit tasks. The experiment described in this article addressed these questions. The results 1) confirm that effective trial-by-trial system switching, although difficult, is possible; 2) suggest that switching between tasks mediated by different memory systems is more difficult than switching between two declarative memory tasks; and 3) point to a serious shortcoming of current category-learning theories.

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