## 2 Related Works
### Cognitive Workload Measurement--A Way of Interruption Cost Evaluation  
The decline in task performance upon interruption is closely correlated with the increase of cognitive workload. Basically, cognitive workload can be feasibly measured by how many and how long critical pieces of information can be held duringinterruptive tasking environments [39]. This memory-dependent, task-related information, which is referred to as the problem state [39], was recognized as a key criterion for measuring cognitive workload: humans can retain limited amounts of problem-state information in single-task processing [40], and the task switching upon interruption incurs additional costs of increasing task completion time and the risk of error [41]. The concept of the problem-state indicates the best timing of engaging interruptions will be at moments of minimal problem state or at the lowest level of cognitive workload, which can be evaluated as the lowest interruption cost. Numerous approaches were employed to evaluate cognitive workloads. Firstly, the subjective approach is traditionally used in various research domains. The workload is measured by directly asking how much workload individuals experience while performing a certain task. The representative example of this approach is the National Aeronautics and Space Administration (NASA) task load index (TLX) method [42]. It assesses and quantifies the degree of workload by combining perceived scores on six subscales and individual weighting of these subscales. This subjective, self-reporting measure assumes that individuals can distinguish the different amounts of workload among tasks. However, the consequent measures indicate a snapshot of recency effects at the end of a task rather than a continuous measurement. Another limitation is that the workload estimate is based on self-reporting, which may not be reliable or accurate. Secondly, behavioral task performance can measure cognitive workload [43; 44]. This approach assumes that the external behavioral performance is a measurable outcome of an individual cognitive task and that poor performance is a good indicator of workload. The measured outcomes may be quantitative or qualitative, and indicate the individual's successful execution of the task. Though task performance and cognitive workload are related to each other, their levels are not necessarily matched. Task performance is maximized when the cognitive workload is optimal, and it can be degraded when the workload is too high or too low. The selection of right tasks and the consideration of individual difference are key aspects in this approach. Thirdly, the physiological approach uses data from heart rate (HR) [45], heart-rate variability (HRV) [46], galvanic skin response (GSR) [47], skin temperature, and respiration rate to indirectly assess how a person uses mental resources to process information and identify the limits of cognitive capabilities. However, these data are obtained by rather obtrusive, invasive methods, such as the wearing or direct connecting of sensors, along with the risk of breaching personal health data. Among these physiological measures, pupil diameter data by eye-tracking technology is advantageous in cognitive workload measurement, because reliable data can be collected in a non-invasive way. A main issue with using pupil diameter data to infer cognitive workload is that physiological changes in the eye are influenced by the difficulty of the engaged task [48; 49; 50]. Pupil diameter is highly responsive to changes in task difficulty, which is called the task-evoked pupillary response (TEPR) [48]. Studies confirmed this relationship between pupillary changes and cognitive workload, and this rapid change in pupil diameter enables the measurement of cognitive workload with minimal discomfort [51; 52]. Recently, [PERSON] et al. [53] asserted that pupil diameter might be a more reliable measure of cognitive workload than heart rate in driving simulator studies.