Virtual reality (VR) environments are increasingly being used by neuroscientists to simulate natural events and social interactions. VR creates interactive, multimodal sensory stimuli that offer unique advantages over other approaches to neuroscientific research and applications. VR's compatibility with imaging technologies such as functional MRI allows researchers to present multimodal stimuli with a high degree of ecological validity and control while recording changes in brain activity. Therapists, too, stand to gain from progress in VR technology, which provides a high degree of control over the therapeutic experience. Here we review the latest advances in VR technology and its applications in neuroscience research.

The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.

The effects of two different kinds of categorizationtraining were investigated. In observational training, observersare presented with a category label and then shown an exemplar from that category. In feedback training, they are shown an exemplar, asked to assign it to a category, and then given feedback about the accuracy of their response. These two types of training were compared as observers learned two types of category structures-those in which optimal accuracy could be achieved via some explicit rule-based strategy, and those in which optimal accuracy required integrating information from separate perceptual dimensions at some predecisional stage. There was an overall advantage for feedback training over observational training, but most importantly, type of training interactedstrongly with type of category structure. With rule-based structures, the effects of training type were small, but with information-integrationstructures, accuracy was substantially higher with feedback training, and people were less likely to use suboptimal rule-based strategies. The implications of these results for current theories of category learning are discussed.

The optimally of multidimensional perceptual categorization performance with unequal base rates and payoffs was examined. In Experiment 1, observers learned simultaneously the category structures and base rates or payoffs. Observers showed conservative cutoff placement when payoffs were unequal and extreme cutoff placement when base rates were unequal. In Experiment 2, observers were trained on the category structures before the base-rate or payoff manipulation. Simultaneous base-rate and payoff manipulations tested the hypothesis that base-rate information and payoff information are combined independently. Observers showed (a) small suboptimalities in base-rate and payoff estimation, (b) no qualitative differences across base-rate and payoff conditions, and (c) support for the hypothesis that base-rate and payoff information is combined independently. Implications for current theories of base-rate and payoff learning are discussed. Categorization is a primary component of many behaviors of all organisms. Rats categorize bits of food as "large" or "small," with small pieces being eaten immediately and large pieces being hoarded (Wishaw, 1990; Wishaw & Tomie, 1989). The red-bellied stickleback categorizes prey by color and pattern, with certain patterns being pursued and others being avoided (Alcock, 1989). Humans categorize speech sounds and handwritten characters to facilitate communication. Medical doctors categorize X-rays to determine whether a tumor is present or absent and to make diagnoses by examining patterns of symptoms or test results. All organisms divide objects and events into separate categories. If they did not perform these tasks with some measure of success, they would die and their species would become extinct. In light of this fact, it is reasonable to hypothesize that in many domains, human (and other organisms*) categorization performance is very nearly optimal (Ashby & Maddox, 1998). Although optimality can be defined in many ways, a common definition is performance that maximizes long-run reward (Green & Swets, 1966). To examine rigorously the optimality of categorization performance, one must identify the basic properties of everyday categorization problems. First, the stimulus can be decomposed into a set of values along multiple basic

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