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We conducted four studies regarding the representation of probabilistic information. Experiments 1 through 3 compared performance on a simulated stock purchase task, in which information regarding stock profitability was probabilistic. Two variables were manipulated: display format for probabilistic information (blurred and colored icons, linguistic phrases, numeric expressions, and combinations) and specificity level (in which the number and size of discrete steps into which the probabilistic information was mapped differed). Results indicated few performance differences attributable to display format; however, performance did improve with greater specificity. Experiment 4, in which participants generated membership functions corresponding to three display formats, found a high degree of similarity in functions across formats and participants and a strong relationship between the shape of the membership function and the intended meaning of the representation. These results indicate that participants can successfully interpret nonnumeric representations of uncertainty and can use such representations in a manner similar to the way numeric expressions are used in a decision-making task. Actual or potential applications of this research include the use of graphical representations of uncertainty in systems such as command and control and situation displays.

INTRODUCTION Cognitive work analysis (CWA) and associated techniques focusing on the analysis of work domains are becoming popular methods in the analysis and design of complex systems (Burns, Barsalou, Handlei; […], As methods in cognitive work analysis become more widely applied, questions regarding the impact of modeling choices and similarities in modeling efforts across projects and domains are increasingly relevant. However, no explicit comparison of models of similar systems has been reported. This paper compares independently developed work domain analysis (WDA) models of two command and control environments. Similarities in model content and the types of nodes included provide evidence that WDA techniques can capture fundamental elements regarding purposes and constraints. These points of agreement provide a common starting point for developing work domain representations of military command and control systems. The comparison also revealed differences between the models. Although differences in content reflected differences in scope of coverage and level of detail, other differences corresponded to more fundamental choices in modeling approach. These included the treatment of sensors, level of integration in the model, and representation of particular abstract constraints. Examination of these more fundamental differences pointed to important degrees of freedom in how to represent a WDA and clarified the implications of these modeling choices for guiding design. Actual or potential applications of this research include aiding analysts in making work domain modeling choices as well as producing work domain models of command and control environments.

INTRODUCTION Motivation A common paradigm in human-automation interaction requires a human to make a judgment, in parallel with an automated system, and then to consider, accept, or reject the automated […], Methodologies for assessing human judgment in complex domains are important for the design of both displays that inform judgment and automated systems that suggest judgments. This paper uses the n-system lens model to evaluate the impact of displays on human judgment and to explicitly assess the similarity between human judgments and a set of potential judgment algorithms for use in automated systems. First, the need for and concepts underlying judgment analysis are outlined. Then the n-system lens model and its parameters are formally described. This model is then used to examine a previously conducted study of aircraft collision detection that had been analyzed using standard analysis of variance methods. Our analysis found the same main effects as did the earlier analysis. However, n-system lens model analysis was able to provide greater insight into the information relied upon for judgments and the impact of displays on judgment. Additionally, the analysis was able to identify attributes of human judgments that were--and were not--similar to judgments produced by automated systems. Potential applications of this research include automated and design and operator training.