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"

Recent constraint-satisfaction models of explanation, analogy, and decision making claim that these processes are influenced by bidirectional constraints that promote coherence. College students were asked to reach a verdict in a complex legal case involving multiple conflicting arguments, including alternative analogies to the target case. Participants rated agreement with the individual arguments both in isolation before seeing the case, and again after reaching a verdict. Assessments of the individual arguments (including the competing analogies) shifted so as to cohere with their emerging verdict. Information about the character of the defendant in the initial case triggered a cascade of "spreading coherence", influencing decisions made about a subsequent case involving very different legal issues. Participants ' memory for their initial positions also shifted so as to cohere with their final positions. The coherence shifts were simulated by a constraint satisfaction model. The results demonstrate that an alogical process of constraint satisfaction can transform highly ambiguous inputs into coherent decisions. Bidirectional Reasoning 3 One of the most deep-rooted assumptions about human reasoning is that the flow of

The distinction between rule-based and similarity-based processes in cognition is of fundamental importance for cognitive science, and has been the focus of a large body of empirical research. However, intuitive uses of the distinction are subject to theoretical difficulties and their relation to empirical evidence is not clear. We propose a ‘core ’ distinction between ruleand similarity-based processes, in terms of the way representations of stored information are ‘matched ’ with the representation of a novel item. This explication captures the intuitively clear-cut cases of processes of each type, and resolves apparent problems with the rule/ similarity distinction. Moreover, it provides a clear target for assessing the psychological and AI literatures. We show that many lines of psychological evidence are less conclusive than sometimes assumed, but suggest that converging lines of evidence may be persuasive. We then argue that the AI literature suggests that approaches which combine rules and similarity are an important new focus for empirical work. © 1998 Elsevier Science B.V. Keywords: Similarity-based process; Rule-based process 1.

Modern theories in cognitive psychology and neuroscience indicate that there are two fundamental ways in which human beings comprehend risk. The “analytic system ” uses algorithms and normative rules, such as the probability calculus, formal logic, and risk assessment. It is relatively slow, effortful, and requires conscious control. The “experiential system ” is intuitive, fast, mostly automatic, and not very accessible to conscious awareness. The experiential system enabled human beings to survive during their long period of evolution and remains today the most natural and most common way to respond to risk. It relies on images and associations, linked by experience to emotion and affect (a feeling that something is good or bad). This system represents risk as a feeling that tells us whether it’s safe to walk down this dark street or drink this strange-smelling water. Proponents of formal risk analysis tend to view affective responses to risk as irrational. Current wisdom disputes this view. The rational and the experiential systems operate in parallel and each seems to depend on the other for guidance. Studies have demonstrated that analytic reasoning cannot be effective unless it is guided by emotion and affect. Rational decision making requires proper integration of both modes of

Kerr, MacCoun, & Kramer, 1996). Teamwork is not guaranteed to provide either of these advantages. With respect to (1) combining complementary inputs, increasing the size of an organization tends to reduce its overall efficiency unless there is also an increase in departmentalization and standardization of tasks (Blau, 1970). The latter features, however, reduce flexibility of response in a changing or novel environment (Donaldson, 1995). A related problem is goal displacement, in which specialized units lose sight of the larger organizational purpose, and pursue their own goals as if they were fixed ends rather than means, which should be reevaluated when conditions change (Scott, 1998). With respect to (2) better decisions, groups may be affected by socialization biases, such as groupthink, which induce conformity rather than diversity of thought (Janus, 1972; March, 1996.). For this reason, group decisions tend to be better when individuals think about the problem independently befo

How does the domain or subject matter of a decision problem affect the outcome of the decision? Although decision-making research typically dismisses content as merely a cover story, the present research shows that it plays a fundamental role in the decision process by influencing the information processing that underlies it. An experiment is reported in which the same basic decision problem was presented in several content domains (legal traffic tickets, academic course grades, stock investments, and casino gambling). The changes in content led to changes in both strategies and mental representations, which in turn led to changes in decision outcomes, even though measures of the subjective utilities of the options remained unchanged. � 2001 Academic Press Life is a gamble. At least that is the expressed wisdom of many philosophers and behavioral scientists. This precept has led to a large literature of decisionmaking studies in which people are asked to make ratings and choices of monetary gambles like those they would encounter in casinos. Lopes (1983) described the simple monetary gamble as playing the same role in decision research that the fruit fly occupies in genetics. The prevailing view in the These data were presented at the 1998 Annual Meetings of the Psychonomic Society and Society for Judgment and Decision Making. This study was supported by NSF Grant SBR 9816458, NIMH Grant R01 MH58362, and the Psychology Department at the University of Colorado at Boulder. It is based on the doctoral dissertation of the first author. The authors thank Walter Kintsch for advice on this project as well as two anonymous reviewers who made substantial suggestions for revisions. Address correspondence and reprint requests to David A. Rettinger, Psychology Department,

Perrow (1984) provides the following account of what he calls a “normal ” accident (see Figure 1): “On a beautiful night in October 1978, in the Chesapeake Bay, two vessels sighted one another visually and on radar. On one of them, the Cost Guard cutter training vessel Cuyahoga, the captain (a chief warrant officer) saw the other ship up ahead as a small object on the radar, and visually he saw two

This paper outlines a framework for analyzing how decision makers achieve “situation awareness ” in complex, dynamic and uncertain situations. We use this framework to analyze a typical case of human “error ” in the command and control of a complex system. Our analysis shows that decision errors in this and other cases can be characterized as “normal ” (i.e., rational) consequences of the decision makers’ mental models. We conclude by suggesting that a mental models approach can provide unifying insights into the “heuristics and biases ” proposed by previous researchers. _____________________________________________________________________________________________ 1

This paper presents a theory of reasoning with evidence in order to determine the facts in a criminal case. The focus is on the process of proof, in which the facts of the case are determined, rather than on related legal issues, such as the admissibility of evidence. In the literature, two approaches to reasoning with evidence can be distinguished, one argument-based and one storybased. In an argument-based approach to reasoning with evidence, the reasons for and against the occurrence of an event, e.g., based on witness testimony, are central. In a story-based approach, evidence is evaluated and interpreted from the perspective of the factual stories as they may have occurred in a case, e.g., as they are defended by the prosecution. In this paper, we argue that both arguments and narratives are relevant and useful in the reasoning with and interpretation of evidence. Therefore a hybrid approach is proposed and formally developed, doing justice to both the argumentbased and the narrative-based perspective. By the formalization of the theory and the associated graphical representations, our proposal is the basis for the design of software developed as a tool to make sense of the evidence in complex cases.

Abstract. We propose a new focus in language design where languages provide constructs that not only describe the computation of results, but also produce explanations of how and why those results were obtained. We posit that if users are to understand computations produced by a language, that language should provide explanations to the user. As an example of such an explanation-oriented language we present a domain-specific language for explaining probabilistic reasoning, a domain that is not well understood by non-experts. We show the design of the DSL in several steps. Based on a story-telling metaphor of explanations, we identify generic constructs for building stories out of events, and obtaining explanations by applying stories to specific examples. These generic constructs are then adapted to the particular explanation domain of probabilistic reasoning. Finally, we develop a visual notation for explaining probabilistic reasoning. 1