Distinctions have been proposed between systems of reasoning for centuries. This article distills properties shared by many of these distinctions and characterizes the resulting systems in light of recent findings and theoretical developments. One system is associative because its computations reflect similarity structure and relations of temporal contiguity. The other is "rule based " because it operates on symbolic structures that have logical content and variables and because its computations have the properties that are normally assigned to rules. The systems serve complementary functions and can simultaneously generate different solutions to a reasoning problem. The rule-based system can suppress the associative system but not completely inhibit it. The article reviews evidence in favor of the distinction and its characterization. One of the oldest conundrums in psychology is whether people are best conceived as parallel processors of information who operate along diffuse associative links or as analysts who operate by deliberate and sequential manipulation of internal representations. Are inferences drawn through a network of learned associative pathways or through application of a kind of "psychologic"

Is the mind, by design, predisposed against performing Bayesian inference? Previous research on base rate neglect suggests that the mind lacks the appropriate cognitive algorithms. However, any claim against the existence of an algorithm, Bayesian or otherwise, is impossible to evaluate unless one specifies the information format in which it is designed to operate. The authors show that Bayesian algorithms are computationally simpler in frequency formats than in the probability formats used in previous research. Frequency formats correspond to the sequential way information is acquired in natural sampling, from animal foraging to neural networks. By analyzing several thousand solutions to Bayesian problems, the authors found that when information was presented in frequency formats, statistically naive participants derived up to 50 % of all inferences by Bayesian algorithms. Non-Bayesian algorithms included simple versions of Fisherian and Neyman-Pearsonian inference. Is the mind, by design, predisposed against performing Bayesian inference? The classical probabilists of the Enlightenment, including Condorcet, Poisson, and Laplace, equated probability theory with the common sense of educated people, who were known then as “hommes éclairés.” Laplace (1814/1951) declared that “the theory of probability is at bottom nothing more than good sense reduced to a calculus which evaluates that which good minds know by a sort of instinct,

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Research on people’s confidence in their general knowledge has to date produced two fairly stable effects, many inconsistent results, and no comprehensive theory. We propose such a comprehensive framework, the theory of probabilistic mental models (PMM theory). The theory (a) explains both the overconfidence effect (mean confidence is higher than percentage of answers correct) and the hard-easy effect (overconfidence increases with item difficulty) reported in the literature and (b) predicts conditions under which both effects appear, disappear, or invert. In addition, (c) it predicts a new phenomenon, the confidence-frequency effect, a systematic difference between a judgment of confidence in a single event (i.e., that any given answer is correct) and a judgment of the frequency of correct answers in the long run. Two experiments are reported that support PMM theory by confirming these predictions, and several apparent anomalies reported in the literature are explained and integrated into the present framework. Do people think they know more than they really do? In the last 15 years, cognitive psychologists have amassed a large and apparently damning body of experimental evidence on overconfidence in knowledge, evidence that is in turn part of an even larger and more damning literature on socalled cognitive biases. The cognitive bias research claims that people are naturally prone to making mistakes in reasoning and memory, including the mistake of overestimating their knowledge.

the heuristics-and-biases approach to statistical reasoning is and is not about. At issue is the imposition of unnecessarily narrow norms of sound reasoning that are used to diagnose so-called cognitive illusions and the continuing reliance on vague heuristics that explain everything and nothing. D. Kahneman and A. Tversky (1996) incorrectly asserted that Gigerenzer simply claimed that frequency formats make all cognitive illusions disappear. In contrast, Gigerenzer has proposed and tested models that actually predict when frequency judgments are valid and when they are not. The issue is not whether or not. or how often, cognitive illusions disappear. The focus should be rather the construction of detailed models of cognitive processes that explain when and why they disappear. A postscript responds to Kahneman and Tversky's (1996) postscript. I welcome Kahneman and Tversky's (1996) reply to my critique (e.g., Gigerenzer, 1991, 1994; Gigerenzer & Murray, 1987) and hope this exchange will encourage a rethinking of research strategies. I emphasize research strategies, rather than specific empirical results or even explanations of those results, because I believe that this debate is fundamentally about what

Emotional reasoning can be an important contribution to auto- mated tutoring and training systems. This paper describes mile, a model of emotional reasoning that builds upon existing approaches and significantly generalizes and extends their capabilities. The main contribution is to show how an explicit planning model allows a more general treatment of several stages of the reasoning process. The model supports educational applications by allowing agents to appraise the emotional significance of events as they relate to students' (or their own) plans and goals, model and predict the emotional state of others, and alter behavior accordingly.

This paper has two objectives. First, we will argue that the mutability of conceptual fea- tures can be represented as a single, multiple-valued dimension. We will show that the fea- tures of a concept can be reliably ordered with respect to the degree to which people are willing to transform the feature while retaining the integrity of a representation; i.e., that a number of conceptual tasks, all of which require people to transform conceptual features, produce similar orderings. Following Medin and Shoben (1988), these tasks have in common that they ask people to consider an object that is missing a feature but is otherwise intact (e.g., a real chair without a seat)

Similarity-based and rule-based accounts of cognition are often portrayed as opposing accounts. In this paper we suggest that in learning and development, the process of comparison can act as a bridge between similarity-based and rule-based processing. We suggest that comparison involves a process of structural alignment and mapping between two representations. This kind

Singular value decomposition (SVD) can be viewed as a method for unsupervised training of a network that associates two classes of events reciprocally by linear connections through a single hidden layer. SVD was used to learn and represent relations among very large numbers of words (20k-60k) and very large numbers of natural text passages (1k70k) in which they occurred. The result was 100-350 dimensional "semantic spaces" in which any trained or newly added word or passage could be represented as a vector, and similarities were measured by the cosine of the contained angle between vectors. Good accuracy in simulating human judgments and behaviors has been demonstrated by performance on multiple-choice vocabulary and domain knowledge tests, emulation of expert essay evaluations, and in several other ways. Examples are also given of how the kind of knowledge extracted by this method can be applied.

This article reviews the now extensive research literature addressing the impact of accountability on a wide range of social judgments and choices. It focuses on 4 issues: (a) What impact do various accountability ground rules have on thoughts, feelings, and action? (b) Under what conditions will accountability attenuate, have no effect on, or amplify cognitive biases? (c) Does accountability alter how people think or merely what people say they think? and (d) What goals do accountable decision makers seek to achieve? In addition, this review explores the broader implications of accountability research. It highlights the utility of treating thought as a process of internalized dialogue; the importance of documenting social and institutional boundary conditions on putative cognitive biases; and the potential to craft empirical answers to such applied problems as how to structure accountability relationships in organizations. Accountability is a modern buzzword. In education (Fairchild &