The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R.M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity. Models of multidimensional perceptual classification have grown increasingly powerful and sophisticated in recent years, providing detailed quantitative accounts of patterns of classifi-cation learning, transfer, and generalization (e.g., Anderson,

Historically, at least 3 methodological problems have dogged experimental social psychology: the experimental control–mundane realism trade-off, lack of replication, and unrepresentative sampling. We argue that immersive virtual environment technology (IVET) can help ameliorate, if not solve, these methodological problems and, thus, holds promise as a new social psychological research tool. In this article, we first present an overview of IVET and review IVET-based research within psychology and other fields. Next, we propose a general model of social influence within immersive virtual environments and present some preliminary findings regarding its utility for social psychology. Finally, we present a new paradigm for experimental social psychology that may enable researchers to unravel the very fabric of social interaction.

this article may be addressed to either Michael Kahana or Robert Sekuler, Volen National Center for Complex Systems, MS 013, Brandeis University, Waltham, MA 02254-9110. E-mail may be sent to kahana @brandeis.edu or sekuler@brandeis.edu plex multidimensional stimulus spaces (Nosofsky, 1992; Maddox & Ashby, 1996; Ashby & Perrin, 1988), with decision rules that can predict performance in a variety of classification paradigms (Nosofsky & Palmeri, 1998; Nosofsky & Alfonso-Reese, 1999; Maddox & Ashby, 1996). Although models of classification and models of visual discrimination share many assumptions about stimulus representation and subjects' decision rules, models of classification have been primarily developed to explain subjects' classification of combinations of simple geometric forms, whereas models of discrimination have been developed to explain subjects ' discrimination of elemental visual stimuli, including sinusoidal luminance gratings. Because such stimuli can be combined to synthesize more complex images such as textures and natural scenes, they represent a natural test-bed for assessing theories' power and generalizability

General recognition theory (GRT) is a multivariate generalization of signal detection theory. Past versions of GRT were static and lacked a process interpretation. This article presents a stochastic version of GRT that models moment-by-moment fluctuations in the output of perceptual channels via a multivariate diffusion process. A decision stage then computes a linear or quadratic function of the outputs from the perceptual channels, which drives a univariate diffusion process that determines the subject's response. Conditions are established under which the stochastic and static versions of GRT make identical accuracy predictions. These equivalence relations show that traditional estimates of perceptual noise may often be corrupted by decisional influences. 2000 Academic Press General recognition theory (GRT), which was first introduced by Ashby and Townsend (1986), is a multivariate generalization of signal detection theory (e.g.,

The authors compared the exemplar-based random-walk (EBRW) model of Nosofsky and Palmeri

Four observers completed perceptual matching, identification, and categorization tasks using separable-dimension stimuli. A unified quantitative approach relating perceptual matching, identification, and categorization was proposed and tested. The approach derives from general recognition theory (Ashby & Townsend, 1986) and provides a powerful method for quantifying the separate influences of perceptual processes and decisional processes within and across tasks. Good accounts of the identification data were obtained from an initial perceptual representation derived from perceptual matching. The same perceptual representation provided a good account of the categorization data, except when selective attention to one stimulus dimension was required. Selective attention altered the perceptual representation by decreasing the perceptual variance along the attended dimension. These findings suggest that a complete understanding of identification and categorization performance requires an understanding of perceptual and decisional processes. Implications for other psychological tasks are discussed. An important goal of psychological inquiry is to understand how behavior is influenced by the environmental stimulation and the task at hand. Information about the environment

this article should be addressed to W. Todd Maddox, Department of Psychology, Mezes Hall 330 Mail Code B3800, University of Texas, Austin, Texas, 78712. E-mail: maddox@psy.utexas.edu

Speeded perceptual classification experiments were conducted to distinguish among the predictions of exemplar-retrieval, decision-boundary, and prototype models. The key manipulation was that across conditions, individual stimuli received either probabilistic or deterministic category feedback. Regardless of the probabilistic feedback, however, an ideal observer would always classify the stimuli by using an identical linear decision boundary. Subjects classified the probabilistic stimuli with lower accuracy and longer response times than they classified the deterministic stimuli. These results are in accord with the predictions of the exemplar model and challenge the predictions of the prototype and decision-boundary models. A fundamental issue in the field of perceptual classification concerns the manner in which people represent categories in memory and the decision processes that they use for making classification judgments. Among the major formal models of perceptual classification are exemplar-retrieval, prototype, and decision-boundary models. According to exemplar-retrieval models (Hintzman, 1986; Medin & Schaffer, 1978; Nosofsky, 1986), people represent categories by storing individual exemplars of categories in memory, and they make classification decisions on the basis of the similarity of test items to these stored exemplars. According to prototype models (Posner & Keele, 1968; Reed,

Twelve male listeners categorized 54 synthetic vowel stimuli that varied orthogonally in F2 and F3 on a BARK scale into the American English vowel categories /I/, /U/, and / ˛ /. A neuropsychological model of visual categorization, called the Striatal Pattern Classifier (SPC; [1]) is generalized to the auditory domain, and applied separately to the data from each observer. Performance of the SPC is compared with the successful Normal A Posteriori Probability model (NAPP; [2], [3]) of auditory categorization. Versions of the SPC and NAPP that assume linear response region partitions provided similar accounts of the data. Nonlinear versions of both models provided only small improvements in fit. 1.

Do people respond to a virtual human in the same way they would to a real human? To answer this question, we designed a study which replicates a classical test of human-human social interaction. Particularly, we chose to replicate the social facilitation/social inhibition effects. Social facilitation/inhibition theory simply states that when in the presence of others, people perform learned tasks better and novel tasks worse. Participants first learned a task and were then randomly assigned to perform the same task or a novel task either alone, in the presence of a real human, or in the presence of a virtual human. Our results showed that people reacted to the virtual human similarly to the way they reacted to the real human. In particular, female participants performing in the presence of the virtual human demonstrated the social inhibition effect. We also found that more women learned the novel task when alone than when being observed by either a human or a virtual human.