Abstract
A novel approach to measure the self-generated complexity of human-machine interaction is presented. A complexity measure that relies solely on information-theoretic quantities is defined. An efficient estimation procedure for the complexity measure is introduced. The theoretical framework is validated by a study of human interaction with electronic map displays. Thirty experienced master mariners participated. The task was to search for multiple vessel symbols under time pressure. Samples from the visual scanpath, as well as manual responses, were acquired. The workloads due to time pressure, the number of symbol clusters on the display, and the map scenario were systematically varied. The results of an analysis of variance (ANOVA, /spl alpha/=0.05) show a significant complexity decrease when the time pressure (or workload) is increased. The workload effect on the complexity of master mariners' visual scanpath was stronger than on their manual response. There was also a significant effect of the number of symbol clusters on the display: a display with two clusters showed a significantly higher search complexity for manual response than a nonclustered display.
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