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Characteristics of Fluent Skills in a Complex, Dynamic Problem-Solving Task
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.