This symposium is concerned with models of decision-making under time constraints. While decision making has been the subject of considerable investigation in cognitive science for a number of years, the earlier work tended to concentrate on abstracted decontextualized laboratory models. More recently, however, a number of approaches have evolved that attempt to study real-world, real-time cognitive behavior in complex dynamic environments. All four participants in this symposium will focus on decision-making under emergency and/or time constrained real-life circumstances such as 9-1-1 emergency assistance, air traffic control, and trauma management. These studies involve rapid judgments using partial and sometimes unreliable information that affect public health and safety. The presentations bring together multidisciplinary approaches in cognitive science and a large range of theoretical perspectives, from standard models of information processing to models of situated cognition. These different perspectives make a considerable difference in the kinds of phenomena that we need to examine i n empirical research. The study of real-world dynamic environments necessitates the use of complex methods of data collection and analysis, from the use of videotapes to actual participant observations. These various theoretical and methodological approaches are used by the participants (Hunt, Patel, Horst, and Whalen) to study decision-making in "messy", realistic activities, requiring immediate intervention. The approaches are relevant to other areas where similar issues of complexity and urgency must be dealt with.

Li Montreal, nurses respond to 9-1-1 emergency calls for medical help, backed up by physicians when needed. In this context, they have to make rapid decisions based on limited and sometimes unreliable information. The purpose of this study was to describe the decision-making processes used by nurses in telephone triage and to examine the relations among these processes in relation to nurses' characteristics and performance. The study was conducted in real emergency conditions. The sample included 34 nurses and 50 calls. Each call was transcribed and subjected to performance evaluation and content analysis. This paper focuses on the cognitive analyses of two protocols associated with different outcomes. The results show that nurses' decision-making in triage situations are often based on surface features (patterns of symptoms) rather than the underlying pathophysiology, particularly in high urgency cases. High performance was related to decisions based on the evaluation of the whole emergency situation. The contribution of training and the effects of experience on triage performance are discussed.

It is well established that there are declines in basic cognitive functions associated with aging. However, for individuals with extensive knowledge in a particular domain (e.g. experts), there does not appear to be age-related limitations in performance. Although expert performance relies on certain fundamental cognitive processes, such as information processing and memory capacity, the strategy with which an expert maintains a high level of functioning in his/her domain may be altered. One may view this alteration as a sign of compensation for age-related limitation, or one may deem that this alteration of strategies is due to the natural course of extensive practice in the field. The aim of this paper is to explore diagnostic reasoning processes in an aging medical specialist. Specifically, this study explores what aspects of performance approximates that of a younger expert, and what aspects deviate from the current model of expertise in medicine.

This paper investigates the process of hypothesis generation and the coordination of hypothesis and evidence in medical diagnostic tasks. Two issues are addressed: the generation of hypothesis and the directionality of reasoning. Two problems whose initial presentation suggested an initial hypothesis were presented to subjects with different degrees of expertise in clinical medicine. When faced with contradictory evidence against the initial hypothesis, 1) early novices either modified the initial hypothesis, or ignored, or reinterpreted the cues in the problem to fit the hypothesis; 2) intermediate novices generated concurrent hypotheses to account for different sets of data; and 3) advanced novices generated several initial hypotheses and subsequently narrowed the hypothesis space by generating a single coherent diagnostic hypothesis. All subjects, used a mixture of forward reasoning and backward reasoning. A more forward-directed reasoning was related to diagnostic accuracy. These results on diagnostic reasoning are discussed in relation to findings on scientific reasoning.

In this paper we examine the role of domain knowledge in the process of hypothesis generation and problem representation during diagnostic reasoning. An on-line task environment and the combination of discourse and protocol analysis techniques were used to test the differences between two groups of experts solving a clinical problem. The groups consisted of high domain knowledgesubjects (HDK) -endocrinologists- and low domain-knowledge (LDK) subjects cardiologists-. TTie results show that HDK subjects used a more efficient process of diagnostic reasoning as generated a more coherent representation of the problem. A two-stage model describing the process of hypothesis generation was proposed to explain the differences in theprocess of hypothesis generation.

Clinical problem solving involves both diagnostic and predictive reasoning.Diagnostic reasoning is characterized by inference from observations to hypotheses; predictive reasoning, by inference from hypotheses to observations. We investigate the use of such strategiesby medical students at three levels of training in explaining the underlying pathophysiology of aclinical case. Our results show that without a sound, pre-existing disease classification, the use ofbasic biomedical knowledge interferes with diagnostic reasoning; however, with sound classification, biomedical knowledge facilitates both diagnostic and predictive reasoning.