Abstract Computerized systems are taking on increasingly complex tasks. Consequently, monitoring automated computerized systems is becoming increasingly demanding for human operators, which is particularly relevant in time-critical situations. A possible solution might be adapting human–computer interfaces (HCI) to the operators’ cognitive load. Here, we present a novel approach for theory-based measurement of cognitive load based on tracking eye movements of 42 participants while playing a serious game simulating time-critical situations that required resource management at different levels of difficulty. Gaze data was collected within narrow time periods, calculated based on log data interpreted in the light of the time-based resource-sharing model. Our results indicated that eye fixation frequency, saccadic rate, and pupil diameter significantly predicted task difficulty, while performance was best predicted by eye fixation frequency. Subjectively perceived cognitive load was significantly associated with the rate of microsaccades. Moreover our results indicated that more successful players tended to use breaks in gameplay to actively monitor the scene, while players who use these times to rest are more likely to fail the level. The presented approach seems promising for measuring cognitive load in realistic situations, considering adaptation of HCI.
Sevcenko et al. (Mon,) studied this question.