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.

计算机化系统正在承担越来越复杂的任务。因此，监控自动化计算机化系统对操作人员的要求越来越高，这在时间紧迫的情况下尤为重要。一种可能的解决方案是使人机界面 (HCI) 适应操作员的认知负荷。在这里，我们提出了一种基于理论的认知负荷测量新方法，该方法基于跟踪 42 名参与者的眼球运动，同时玩一个严肃的游戏，模拟时间紧迫的情况，需要不同难度的资源管理。凝视数据是在狭窄的时间段内收集的，根据基于时间的资源共享模型解释的日志数据计算得出。我们的结果表明，眼睛注视频率、扫视率、和瞳孔直径显着预测任务难度，而眼睛注视频率最好地预测性能。主观感知的认知负荷与微跳率显着相关。此外，我们的结果表明，更成功的玩家倾向于利用游戏中的休息时间来主动监控场景，而利用这些时间休息的玩家更有可能无法通关。考虑到 HCI 的适应性，所提出的方法似乎有望在现实情况下测量认知负荷。而利用这些时间休息的玩家更有可能无法通关。考虑到 HCI 的适应性，所提出的方法似乎有望在现实情况下测量认知负荷。而利用这些时间休息的玩家更有可能无法通关。考虑到 HCI 的适应性，所提出的方法似乎有望在现实情况下测量认知负荷。