Decision theory has been characterized, for much of its history, by a debate about whether human decision processes are inherently flawed. The remarkable part of this debate is that for virtually its entire duration, it has been conducted without reference to detailed data on how people actually make decisions in everyday settings. In recent years, this issue has come to the forefront in the work of Cohen (1981); Barwise and Perry (1983); Klein, Orasanu, Calderwood, and Zsambok (1993); and others, who have pointed out the fundamental differences between decision making as it has been studied using traditional decision theory and as it occurs in socially situated naturalistic settings. The resulting naturalistic decision theory has emphasized highly detailed, almost ethnographic, studies of decision processes in specific domains. This had resulted in dense data but primarily prose representations and analyses.In parallel to the rise of naturalistic decision theory, cognitive science and human-computer interaction (HCI) researchers were developing increasingly powerfid analysis methods that collectively were called cognitive task analysis techniques. The purpose of these techniques was to analyze and model the cognitive processes that gave rise to human task performance in specific domains, as the basis for design and evaluation of computer-based systems and their user interfaces. The Tactical Decision Making Under Stress (TADMUS) project provided a unique opportunity for these two avenues of inquiry to come together. This chapter describes research that combined the highly formal methods and tools of the HCI community with the theoretical orientation of naturalistic decision theory. The aim of the research reported here was to create a detailed and domain-The authors acknowledge the contributions made by Janine k c e l l to the research reported here as well as the cooperation and effort of the many individuals who acted as participants in the data collection effort. The efforts of Don MacConkey and John Pollen in supporting the data collection and analysis effort on the Navy side are also gratefully acknowledged.
Recording transcripts of human-computer interaction can be a very time-consuming activity. This demonstration presents a new technology to automatically capture such transcripts in Open Systems environments (e.g., from graphical user interfaces running on the X Window System). This technology forms an infrastructure for performing distributed usability testing and human-computer interaction research, by providing integrated data capture, storage, browsing, retrieval, and export capabilities. It may lead to evaluation cost reductions throughout the software development life cycle.
This paper defines a new role for expert models in intelligent embedded training — guiding practice. The integration of problem-based practice with focused, automated instruction has long proven elusive in training systems for complex real-world domains. The training strategy of ‘guided practice’ offers a way to merge the approaches of traditional simulation-based practice and intelligent tutoring's knowledge tracing. The performance of the trainee is dynamically assessed against scenario-specific expectations and performance standards, which are generated during the simulation by embedded models of expert operators. This research developed an executable cognitive model capable of solving realistic simulation scenarios in an expert-level manner, identified and implemented modifications and extensions to this baseline model needed to generate dynamic and adaptive expectations of future trainee actions, and developed means of providing cognitive state information for use in (separate) diagnostic processes, without resorting to full-scale knowledge tracing methods.
Human tactical decision making in Naval Anti-Air Warfare (AAW) is time-critical and is performed in a multiple-task, team-based environment. These aspects make this domain extremely challenging for traditional cognitive modeling techniques. The COGNET ((COGnition as a NEtwork of Tasks) framework, however, is inherently designed for real-time, multi-tasking work, and, with extensions to accommodate team decision processes, proved suitable for modeling AAW decision making in the Navy's Tactical Decision Making Under Stress (TADMUS) program. A COGNET model of AAW domain expertise is described, along with Decision-Support System (DSS) design principles derived from the COGNET AAW model and the underlying COGNET framework.
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