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We examined critical characteristics of fluent cognitive skills, using the Georgia Tech Aegis Simulation Program, a tactical decision-making computer game that simulates tasks of an anti-air-warfare coordinator. To characterize learning, we adopted the unit-task analysis framework, in which a task is decomposed into several unit tasks that are further decomposed into functional-level subtasks. Our results showed that learning at a global level could be decomposed into learning smaller component tasks. Further, most learning was associated with a reduction in cognitive processes, in which people make inferences from the currently available information. Eye-movement data also revealed that the time spent on task-irrelevant regions of the display decreased more than did the time spent on task-relevant regions. In sum, although fluency in dynamic, complex problem solving was achieved by attaining efficiency in perceptual, motor, and cognitive processes, the magnitude of the gains depended on the preexisting fluency of the component skills. These results imply that a training program should decompose a task into its component skills and emphasize those components with which trainees have relatively little prior experience. Actual or potential applications of this research include learning and training of complex tasks as well as evaluation of performance on those tasks.
INTRODUCTION
In the field of cognitive psychology, a number of theories have emerged that describe skill acquisition in laboratory tasks quite successfully. However, it is not clear how these theories would generalize to the solution of complex realworld tasks. In this paper, we extend unit-task analysis (Card, Moran, & Newell, 1983) of skill acquisition to a synthetic task that has some of the complexity of real-world tasks. We picked the Georgia Tech Aegis Simulation Program (GT-ASP; Hodge et al, 1995), which is a tactical decision-making computer game that simulates tasks of anti-air-warfare coordinators (AAWCs) on board U.S. Navy cruisers and destroyers. A participant assumes the role of an AAWC, who monitors a radar screen for unknown aircraft, requests and collects information, and updates the identity of the aircraft. If an unknown aircraft turns out to be hostile, the AAWC keeps monitoring its trajectory and issues a military action in accordance with the rules of engagement. Although vastly simplified from the actual AAWC workstation, GT-ASP is a reasonably realistic cognitive task with medium fidelity to the...