Exemplar-similarity models such as the exemplar-based random-walk (EBRW) model (Nosofsky & Palmeri, 1997a) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to explain relations between classification and recognition. However, a major gap in research is that the models have not been tested on their ability to provide a theoretical account of RTs and other aspects of performance in the classic Sternberg (1966) short-term memory-scanning paradigm, perhaps the most venerable of all recognition-RT tasks. The present research fills that gap by demonstrating that the EBRW model accounts in natural fashion for a wide variety of phenomena involving diverse forms of short-term memory scanning. The upshot is that similar cognitive operating principles may underlie the domains of multidimensional classification and short-term, old-new recognition.

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mentalarchitecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT distribution data associated with individual stimuli in tasks of speeded perceptual classification.A fundamental issue in cognitive science concerns the manner in which people represent categories in memory and the decision processes that they use to determine category membership. In early research in the field, it was assumed that people represent categories in terms of sets of logical rules. Research focused on issues such as the difficulty of learning different rules and on the hypothesis-testing strategies that might underlie rule learning (e.g., Bourne, 1970; Levine, 1975;Neisser & Weene, 1962;Trabasso & Bower, 1968). Owing to the influence of researchers such as Posner and Keele (1968) and Rosch (1973), who suggested that many natural categories have "ill-defined" structures that do not conform to simple rules or definitions, alternative theoretical approaches were developed. Modern theories of perceptual classification, for example, include exemplar models and decision-bound models.Correspondence concerning this article should be addressed to Robert Nosofsky, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405 (nosofsky@indiana.edu); or to Mario Fific, Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Lentzeallee 94, Berlin, 14195 Berlin Germany (fific@mpib-berlin.mpg.de). Robert Nosofsky, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, nosofsky@indiana.edu Computer source codes for the simulation models used in this article are available at http://www.cogs.indiana.edu/nosofsky/. Publisher's Disclaimer:The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other t...

A growing methodology, known as the systems factorial technology (SFT), is being developed to diagnose the types of information-processing architectures (serial, parallel, or coactive) and stopping rules (exhaustive or self-terminating) that operate in tasks of multidimensional perception. Whereas most previous applications of SFT have been in domains of simple detection and visual-memory search, this research extends the applications to foundational issues in multidimensional classification. Experiments are conducted in which subjects are required to classify objects into a conjunctive-rule category structure. In one case the stimuli vary along highly separable dimensions, whereas in another case they vary along integral dimensions. For the separable-dimension stimuli, the SFT methodology revealed a serial or parallel architecture with an exhaustive stopping rule. By contrast, for the integral-dimension stimuli, the SFT methodology provided clear evidence of coactivation. The research provides a validation of the SFT in the domain of classification and adds to the list of converging operations for distinguishing between separable-dimension and integraldimension interactions.Keywords information processing; response times; classification; mental architecture; integral and separable dimensions A fundamental issue in the psychology of perception concerns how information from multiple dimensions is processed in tasks such as detection, recognition, and classification (e.g., Ashby, 1992;Garner, 1974; Kantowitz, 1974;Lockhead, 1972;Schweickert, 1992;Sternberg, 1969;Townsend, 1984). Consider, for example, a simple detection paradigm in which there is a potential target in the left visual field and one in the right visual field. On each trial, the observer's task is to simply detect whether one of the targets is present. One basic question is whether the processing of the information operates in serial fashion or in parallel fashion. In serial processing, information from each visual field is gathered sequentially, one field at a time. By contrast, in parallel processing, information from both visual fields is gathered simultaneously. A second question is whether the processing is exhaustive or self-terminating. In the self-terminating case, processing would stop as soon as a single target is detected. By contrast, in the exhaustive case, processing would continue until the information has been gathered from both visual fields, regardless of whether a target had already been detected in one of them. A third question of interest in the present research is whether the processing may be coactive. In the present example, the intuition is that information from the separate visual Correspondence concerning this article should be addressed to Mario Fific or Robert M. Nosofsky, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405. E-mail: mfific@indiana.edu or nosofsky@indiana.edu. NIH Public Access NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript fields ma...

Many mental tasks that involve operations on a number of items take place within a few hundred milliseconds. In such tasks, whether the items are processed simultaneously (in parallel) or sequentially (serially) has long been of interest to psychologists. Although certain types of parallel and serial models have been ruled out, it has proven extremely difficult to entirely separate reasonable serial and limitedcapacity parallel models on the basis of typical data. Recent advances in theory-driven methodology now permit strong tests of serial versus parallel processing in such tasks, in ways that bypass the capacity issue and that are distribution and parameter free. We employ new methodologies to assess serial versus parallel processing and find strong evidence for pure serial or pure parallel processing, with some striking apparent differences across individuals and interstimulus conditions.