Corticostriatal contributions to musical expectancy perception.

This study investigates the functional neuroanatomy of harmonic music perception with fMRI. We presented short pieces of Western classical music to nonmusicians. The ending of each piece was systematically manipulated in the following four ways: Standard Cadence (expected resolution), Deceptive Cadence (moderate deviation from expectation), Modulated Cadence (strong deviation from expectation but remaining within the harmonic structure of Western tonal music), and Atonal Cadence (strongest deviation from expectation by leaving the harmonic structure of Western tonal music). Music compared with baseline broadly recruited regions of the bilateral superior temporal gyrus (STG) and the right inferior frontal gyrus (IFG). Parametric regressors scaled to the degree of deviation from harmonic expectancy identified regions sensitive to expectancy violation. Areas within the BG were significantly modulated by expectancy violation, indicating a previously unappreciated role in harmonic processing. Expectancy violation also recruited bilateral cortical regions in the IFG and anterior STG, previously associated with syntactic processing in other domains. The posterior STG was not significantly modulated by expectancy. Granger causality mapping found functional connectivity between IFG, anterior STG, posterior STG, and the BG during music perception. Our results imply the IFG, anterior STG, and the BG are recruited for higher-order harmonic processing, whereas the posterior STG is recruited for basic pitch and melodic processing.

Response processes in information-integration category learning.

Much recent evidence suggests that human category learning is mediated by multiple systems. Evidence suggests that at least one of these depends on procedural learning within the basal ganglia. Information-integration categorization tasks are thought to load heavily on this procedural-learning system. The results of several previous studies were interpreted to suggest that response positions are learned in information-integration tasks. This hypothesis was tested in two experiments. Experiment 1 showed that information-integration category learning was slowed but not disrupted when the spatial location of the responses varied randomly across trials. Experiment 2 showed that information-integration learning was impaired if category membership was signaled by responding to a Yes/No question and the category label had no consistent spatial location. These results suggest that information-integration category learning does not require consistent response locations. In these experiments, a consistent association between a category and a response feature was sufficient. The implication of these results for the neurobiology of information-integration category learning is discussed.

Initial training with difficult items facilitates information integration, but not rule-based category learning.

Previous research has disagreed about whether a difficult cognitive skill is best learned by beginning with easy or difficult examples. Two experiments that clarify this debate are reported. Participants in both experiments received one of three types of training on a difficult perceptual categorization task. In one condition, participants began with easy examples, then moved to examples of intermediate difficulty, and finished with the most difficult examples. In a second condition, this order was reversed, and in a third condition, participants saw examples in a random order. The results depended on the type of categories that participants were learning. When the categories could be learned via explicit reasoning (a rule-based task), the three training procedures were equally effective. However, when the categorization rule was difficult to describe verbally (an information-integration task), participants who began with the most difficult items performed much better than participants in the other two conditions.

A neurobiological theory of automaticity in perceptual categorization.

A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory association cortex to the premotor area that mediates response selection. A longer and slower path projects to the premotor area via the striatum, globus pallidus, and thalamus. A faster, purely cortical path projects directly to the premotor area. The model assumes that the subcortical path has greater neural plasticity because of a dopamine-mediated learning signal from the substantia nigra. In contrast, the cortical-cortical path learns more slowly via (dopamine independent) Hebbian learning. Because of its greater plasticity, early performance is dominated by the subcortical path, but the development of automaticity is characterized by a transfer of control to the faster cortical-cortical projection. The model, called SPEED (Subcortical Pathways Enable Expertise Development), includes differential equations that describe activation in the relevant brain areas and difference equations that describe the 2- and 3-factor learning. A variety of simulations are described, showing that the model accounts for some classic single-cell recording and behavioral results.

The neurobiology of category learning.

Many recent studies have examined the neural basis of category learning. Behavioral neuroscience results suggest that both the prefrontal cortex and the basal ganglia play important category-learning roles; neurons that develop category-specific firing properties are found in both regions, and lesions to both areas cause category-learning deficits. Similar studies indicate that the inferotemporal cortex does not mediate the learning of new categories. The cognitive neuroscience literature on category learning appears contradictory until the results are partitioned according to the type of category-learning task that was used. Three major tasks can be identified: rule based, information-integration, and prototype-distortion. Recent results are consistent with the hypotheses that (a) learning in rule-based tasks requires working memory and executive attention and is mediated by frontal-striatal circuits, (b) learning in information-integration tasks requires procedural memory and is mediated primarily within the basal ganglia, and (c) learning in prototype-distortion tasks depends on multiple memory systems, including the perceptual representation system.

A critical review of habit learning and the Basal Ganglia.

The current paper briefly outlines the historical development of the concept of habit learning and discusses its relationship to the basal ganglia. Habit learning has been studied in many different fields of neuroscience using different species, tasks, and methodologies, and as a result it has taken on a wide range of definitions from these various perspectives. We identify five common but not universal, definitional features of habit learning: that it is inflexible, slow or incremental, unconscious, automatic, and insensitive to reinforcer devaluation. We critically evaluate for each of these how it has been defined, its utility for research in both humans and non-human animals, and the evidence that it serves as an accurate description of basal ganglia function. In conclusion, we propose a multi-faceted approach to habit learning and its relationship to the basal ganglia, emphasizing the need for formal definitions that will provide directions for future research.