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-

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this article we focus on the seemingly contradictory results of two quite different approaches to the problem, one dealing with the properties of visually perceived space and the other with visually directed action

It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined theory of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in multidimensional scaling. The present preliminary study appears to indicate that this concept of representation is computationally viable, and is compatible with psychological and neurobiological data. 1 Introduction A perceptual system confronted with a stimulus must (i) decide whether the stimulus belongs to an already encountered category, and (ii) if necessary, create a new category record for the stimulus a...

The pre-eminent importance of formal or relational factors in perception has been abundantly demonstrated during some forty years of gestalt psychology. It seems extraordinary, therefore, that so little progress has been made (and, indeed, that so little effort has been expended) toward the systematizing and quantifying of such factors. Our most precise knowledge of perception is in those areas which have yielded to psychophysical analysis (e.g., the perception of size, color, and pitch), but there is virtually no psychophysics of shape or pattern. Several difficulties may be pointed out at once: (a) Shape is a multidimensional variable, though it is often carelessly referred to as a "dimension," along with brightness, hue, area, and the like, (b) The number of dimensions necessary to describe a shape is not fixed or constant, but increases with the complexity of the shape, (c) Even if we know how many dimensions are necessary in a given case, the choice of particular descriptive terms (i.e., of referenceaxes in the multidimensional space with which we are dealing) remains a problem; presumably some such terms have more psychological meaningfulness than others.

We examine a number of investigations of perceptual economy or, more specifically, of minimum tendencies and minimum principles in the visual perception of form, depth, and motion. A minimum tendency is a psychophysical finding that perception tends toward simplicity, as measured in accordance with a specified metric. A minimum principle is a theoretical construct imputed to the visual system to explain minimum tendencies. After examining a number of studies of perceptual economy, we embark on a systematic analysis of this notion. We examine the notion that simple perceptual representations must be defined within the "geometric constraints " provided by proximal stimulation. We then take up metrics of simplicity. Any study of perceptual economy must use a metric of simplicity; the choice of metric may be seen as a matter of convention, or it may have deep theoretical and empirical implications. We evaluate several answers to the question of why the visual system might favor economical representations. Finally, we examine several accounts of the process for achieving perceptual economy, concluding that those which favor massively parallel processing are the most plausible. The notions of "simplicity " and "economy" have been used in varied contexts within the sciences (see Sober, 1975). It was a commonplace of classical physics and astronomy that "nature acts by the simplest means. " Euler, Lagrange, Hamilton, and others have shown that the central equations of mechanics can be formulated isoperimetrically (in terms of maximum/minimum solutions). In a broader vein, methodologists have proposed that scientists proceed in accordance with the principle of parsimony, which holds that of two theories with equal empirical adequacy, the simpler theory should be chosen. A century ago Mach 0883/1960, 1919) referred this principle to a psychological preference of the scientific investigator for economy of thought. Finally, psychologists have found a tendency

The process by which the human visual system parses an image into contours, surfaces, and objects—perceptual grouping—has proven difficult to capture in a rigorous and general theory. A natural candidate for such a theory is Bayesian probability theory, which provides optimal interpretations of data under conditions of uncertainty. But the fitofBayesian theory to human grouping judgments has never been tested, in part because methods for expressing grouping hypotheses probabilistically have not been available. This paper presents such methods for the case of contour integration; that is, the aggregation of a sequence of visual items into a “virtual curve. ” Two experiments are reported in which human subjects were asked to group ambiguous configurations of dots (in Exp. 1, a sequence of five dots could be judged to contain a “corner” or not; in Exp. 2, an arrangement of six dots could be judged to fall into two disjoint contours or one smooth contour). The Bayesian theory accounts extremely well for subjects ’ judgments, explaining more than 75 % of the variance in both tasks. The theory thus provides a far more quantitatively precise account of human contour integration than previously possible, allowing a very precise calculation of the subjective goodness of a virtual chain of dots. Because Bayesian theory is inferentially optimal, this finding suggests a “rational justification”—and hence possibly an evolutionary rationale—for some of the rules of perceptual grouping. Perceptual grouping is the process whereby individual items in the visual image are aggregated into larger structures. Grouping is known to influence many low-level visual computations, such as the perception of lightness (Adelson,

Statistical regularities of the environment are important for learning, memory, intelligence, inductive inference, and in fact for any area of cognitive science where an informationprocessing brain promotes survival by exploiting them. This has been recognised by many of those interested in cognitive function, starting with Helmholtz, Mach and Pearson, and continuing through Craik, Tolman, Attneave, and Brunswik. In the current era many of us have begun to show how neural mechanisms exploit the regular statistical properties of natural images. Shepard proposed that the apparent trajectory of an object when seen successively at two positions results from internalising the rules of kinematic geometry, and although kinematic geometry is not statistical in nature, this is clearly a related idea. Here it is argued that Shepard's term, "internalisation", is insufficient because it is also necessary to derive an advantage from the process. Having mechanisms selectively sensitive to the spatio-temporal patterns of excitation commonly experienced when viewing moving objects would facilitate the detection, interpolation, and extrapolation of such motions, and might explain the twisting motions that are experienced. Although Shepard's explanation in terms of Chasles' rule seems doubtful, his theory and experiments illustrate that local twisting motions are needed for the analysis of moving objects and provoke thoughts about how they might be detected.

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker’s personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best

We describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin’s theory of natural selection. Simulations of Bayesian natural selection were found to yield new insights, for example, into the co-evolution of camouflage, color vision, and decision criteria. The Bayesian framework captures and generalizes, in a formal way, many of the important ideas of other approaches to perception and cognition.