Implicit learning is nonepisodic learning of complex information in an incidental manner, without awareness of what has been learned. Implicit learning experiments use 3 different stimulus structures (visual, sequence, and function) and 3 different dependent measures or response modalities (conceptual fluency, efficiency, and prediction and control). Implicit learning may require a certain minimal amount of attention and may depend on attentional and working memory mechanisms. The result of implicit learning is implicit knowledge in the form of abstract (but possibly instantiated) representations rather than verbatim or aggregate representations. Implicit learning shows biases and dissociations in learning different stimulus structures. The dependence of implicit learning on particular brain areas is discussed, some conclusions are drawn for modeling implicit learning, and the interaction of implicit and explicit learning is considered.

The caudate nucleus is commonly active when learning relationships between stimuli and responses or categories. Previous research has not differentiated between the contributions to learning in the caudate and its contributions to executive functions such as feedback processing. We used event-related functional magnetic resonance imaging while participants learned to categorize visual stimuli as predicting "rain" or "sun." In each trial, participants viewed a stimulus, indicated their prediction via a button press, and then received feedback. Conditions were defined on the bases of stimulus-outcome contingency (deterministic, probabilistic, and random) and feedback (negative and positive). A region of interest analysis was used to examine activity in the head of the caudate, body/tail of the caudate, and putamen. Activity associated with successful learning was localized in the body and tail of the caudate and putamen; this activity increased as the stimulus-outcome contingencies were learned. In contrast, activity in the head of the caudate and ventral striatum was associated most strongly with processing feedback and decreased across trials. The left superior frontal gyrus was more active for deterministic than probabilistic stimuli; conversely, extrastriate visual areas were more active for probabilistic than deterministic stimuli. Overall, hippocampal activity was associated with receiving positive feedback but not with correct classification. Successful learning correlated positively with activity in the body and tail of the caudate nucleus and negatively with activity in the hippocampus.

The ability to group items and events into functional categories is a fundamental characteristic of sophisticated thought. It is subserved by plasticity in many neural systems, including neocortical regions (sensory, prefrontal, parietal, and motor cortex), the medial temporal lobe, the basal ganglia, and midbrain dopaminergic systems. These systems interact during category learning. Corticostriatal loops may mediate recursive, bootstrapping interactions between fast reward-gated plasticity in the basal ganglia and slow reward-shaded plasticity in the cortex. This can provide a balance between acquisition of details of experiences and generalization across them. Interactions between the corticostriatal loops can integrate perceptual, response, and feedback-related aspects of the task and mediate the shift from novice to skilled performance. The basal ganglia and medial temporal lobe interact competitively or cooperatively, depending on the demands of the learning task.

The striatum is thought to play an essential role in the acquisition of a wide range of motor, perceptual, and cognitive skills, but neuroimaging has not yet demonstrated striatal activation during nonmotor skill learning. Functional magnetic resonance imaging was performed while participants learned probabilistic classification, a cognitive task known to rely on procedural memory early in learning and declarative memory later in learning. Multiple brain regions were active during probabilistic classification compared with a perceptual-motor control task, including bilateral frontal cortices, occipital cortex, and the right caudate nucleus in the striatum. The left hippocampus was less active bilaterally during probabilistic classification than during the control task, and the time course of this hippocampal deactivation paralleled the expected involvement of medial temporal structures based on behavioral studies of amnesic patients. Findings provide initial evidence for the role of frontostriatal systems in normal cognitive skill learning.

This article examines how independent corticostriatal loops linking basal ganglia with cerebral cortex contribute to visual categorization. The first aspect of categorization discussed is the role of the visual corticostriatal loop, which connects the visual cortex and the body/tail of the caudate, in mapping visual stimuli to categories, including evaluating the degree to which this loop may generalize across individual category members. The second aspect of categorization discussed is the selection of appropriate actions or behaviors on the basis of category membership, and the role of the visual corticostriatal loop output and the motor corticostriatal loop, which connects motor planning areas with the putamen, in action selection. The third aspect of categorization discussed is how categories are learned with the aid of feedback linked dopaminergic projections to the basal ganglia. These projections underlie corticostriatal synaptic plasticity across the basal ganglia, and also serve as input to the executive and motivational corticostriatal loops that play a role in strategic use of feedback.Categorization of people (friend or foe), objects (food or nonfood), and environments (dangerous or safe) is vital for survival in the world. The process of categorization involves both knowledge of category structure and linkage of category membership to behavior. Categorical knowledge must be sufficient to allow the organism to correctly classify each member. Categorization requires an appropriate level of generalization: generalization needs to be sufficient to correctly identify category members that are encountered for the first time, but limited so that nonmembers are not included. Categorization processes must also link category members to appropriate behaviors. For example, take the problem of identifying whether a fruit is good to eat. The organism must have a category of edible fruit acquired from past experience (e.g., the blackberries consumed last summer), and will ideally generalize so that related items are also considered edible (e.g., berries that somewhat differ in size or color), but not over generalize to fruits that are sufficiently novel that they might not be edible (e.g., holly berries). Then the categories must be linked to appropriate behaviors (e.g., ingestion of the berries categorized as safe, and avoidance of the others).To learn new categories, there must be plasticity that allows learning of both representations (acquiring new categories, extending or tuning already acquired categories), and new links between categories and behaviors (both learning new behaviors, and extending previously learned behaviors to new categories). Traditional cognitive psychology approaches to categorization have emphasized the how category structure is represented and learned. This approach has led to a rich literature examining learning of many different forms of category