A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Al-though the rational model considers just I level of categorization, it is shown how predictions can be enhanced by considering higher and lower levels. Considering prediction at the lower, individual level allows integration of this rational analysis of categorization with the earlier rational analysis of memory (Anderson & Milson, 1989). Anderson (1990) presented a rational analysis ot 6 human cog-nition. The term rational derives from similar "rational-man" analyses in economics. Rational analyses in other fields are sometimes called adaptationist analyses. Basically, they are ef-forts to explain the behavior in some domain on the assump-tion that the behavior is optimized with respect to some criteria of adaptive importance. This article begins with a general char-acterization ofhow one develops a rational theory of a particu-lar cognitive phenomenon. Then I present the basic theory of categorization developed in Anderson (1990) and review the applications from that book. Since the writing of the book, the theory has been greatly extended and applied to many new phenomena. Most of this article describes these new develop-ments and applications. A Rational Analysis Several theorists have promoted the idea that psychologists might understand human behavior by assuming it is adapted to the environment (e.g., Brunswik, 1956; Campbell, 1974; Gib-

Introduction Nobody doubts that computers have had a profound influence on the study of human cognition. The very existence of a discipline called Cognitive Science is a tribute to this influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is --- or at least on its -- essential character, as this pertains to Cognitive Science. In this essay I will attempt to comment on both these questions. The first question will bring us to a discussion of the role that computing plays in our understanding of human (and perhaps animal) cognition. I wi

Statistical mimicking issues involving reaction time measures are introduced and discussed in this article. Often, discussions of mimicking have concerned the question of the serial vs. parallel processing of inputs to the cognitive system. We will demonstrate that there are several alternative structures that mimic various existing models in the literature. In particular, single process models have been neglected in this area. When parameter variability is incorporated into single process models, resulting in discrete or continuous mixtures of reaction time distributions, the observed reaction time distribution alone is no longer as useful in allowing inferences to be made about the architecture of the process that produced it. Many of the issues are raised explicitly in examination of four different case studies of mimicking. Rather than casting a shadow over the use of quantitative methods in testing models of cognitive processes, these examples emphasize the importance of examining reaction time data armed with the tools of quantitative analysis, the importance of collecting data from the context of specific process models, and also the importance of expanding the data base to include other dependent measures.

We describe two experiments related to learning new words in context. We study two types of new words: metaphors (for whom a related meaning already exists) and artificial words. The new words were used anaphorically to refer to past objects in the text. For anaphoric metaphors, subjects showed an initial bias to adopt a literal interpretation, which shifted as the experiment progressed. Subjects learned the meaning of the metaphors more rapidly and more accurately. After repeated exposure to the words in appropriate contexts, metaphoric sentences were processed comparably with the sentences made only of familiar words, whereas artificial-word sentences maintained a slight disadvantage. Results suggest that participants used context matching to understand and learn new words. We present a computational model that captures the essential trends in the data obtained from the two experiments.

east begin to see where some of these potential lines of inquiry may lie. The psychological concepts that we believe readers will find familiar in this chapter concern issues of measurement, representation, and task demands. These are issues encountered in nearly all models of psychological phenomena. Specifically, we will ask the following kinds of questions. How do you measure the information in a stimulus when the stimulus is a face? How do we represent sub-categories of stimuli, e.g., for faces, male and Quantitative Models of Face Cognition 3 female, young and old? Finally, how do the demands of the task and the nature of the processor constrain our access to and use of the information in the representation? This chapter is organized as follows. We first give a brief overview of the kinds of tasks we must accomplish with human faces. This defines the nature and diversity of the output that computational models must produce to be considered successful. We next pre

Two basic approaches to explaining the nature of the mind are the rational and mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches on their accounts of how humans learn the variability of categories. The mechanistic model departs in subtle ways from rational principles. In particular, the mechanistic model incrementally updates its estimates of category means and variances through error-driven learning, based on discrepancies between new category members and the current representation of each

The authors present a theory of stochastic interactive parallel processing with special emphasis on channel interactions and their relation to system capacity. The approach is based both on linear systems theory augmented with stochastic elements and decisional operators and on a metatheory of parallel channels ’ dependencies that incorporates standard independent and coactive parallel models as special cases. The metatheory is applied to OR and AND experimental paradigms, and the authors establish new theorems relating response time performance in these designs to earlier and novel issues. One notable outcome is the remarkable processing efficiency associated with linear parallel-channel systems that include mutually positive interactions. The results may offer insight into perceptual and cognitive configural–holistic processing systems. When a person views a work of art in all its complexity, it seems as though all dimensions—color, form, arrangement, perspective, and sharpness of edges—are acting in league. But are they? The antithetical notions of independence and dependence have long played a role in the philosophy and science of human perception and cognition. Whether the focus is on the internal representations that support

Introduction Nobody doubts that computers have had a profound influence on the study of human cognition. The very existence of a discipline called Cognitive Science is a tribute to this influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is --- or at least on its -- essential character, as this pertains to Cognitive Science. In this essay I will attempt to comment on both these questions. The first question will bring us to a discussion of the role that computing plays in our understanding of human (and perhaps animal) cognition. I

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Abstract—By monitoring subjects ’ eye movements during a visual search task, we examined the possibility that the mechanism responsible for guiding attention during visual search has no memory for which locations have already been examined. Subjects did reexamine some items during their search, but the pattern of revisitations did not fit the predictions of the memoryless search model. In addition, a large proportion of the refixations were directed at the target, suggesting that the revisitations were due to subjects ’ remembering which items had not been adequately identified. We also examined the patterns of fixations and compared them with the predictions of a memoryless search model. Subjects ’ fixation patterns showed an increasing hazard function, whereas the memoryless model predicts a flat function. Lastly, we found no evidence suggesting that fixations were guided by amnesic covert scans that scouted the environment for new items during fixations. Results do not support the claims of the memoryless search model, and