## 4 General Discussion
### Practical and Theoretical Implications  
The ACT-R-based task-switching model used in this study has several practical and theoretical implications. First of all, one possible application of our model is as a practical tool for evaluating aid systems in discretionary multitasking environments. Several studies on practical applications related to multitasking [46; 47] have developed systems that support multitasking performance for optimally managing the mental workload involved in task switching through an approach to aid in memory retrieval or decision making. In the task switching model, task switching occurs at two points: when the ongoing task is too difficult relative to the performer's ability and when a subtask in the ongoing task is completed. Task switching at the former point occurs during the execution of a subtask, which can have a negative impact on the management of mental workload. Therefore, the proposed model can be used as a tool to evaluate the degree to which the frequency of task switching at the appropriate time improves when using the abovementioned aid systems. Moreover, because the proposed model includes additional decision-making processes associated with voluntary task switching, it can also assess the manner in which aid systems manage the additional demand from these decision processes.  
Second, in terms of the ACT-R theory, a general ACT-R model continues to maintain the goal related to the task, and task switching in multitasking environments is represented through a pre-scheduled goal change. However, most task switching phenomena are difficult to predict and occur dynamically in accordance with the manner in which the task is processed. Therefore, [PERSON] et al. [14] proposed a modeling approach that can describe adaptive task switching in concurrent multitasking environments. Furthermore, in our study, we proposed a task-switching model that can represent adaptive task switching in sequential multitasking environments.  
This implies that the proposed model could apply ACT-R to expanded domains of more complex multiple tasks in discretionary multitasking, in terms of theoretical implications. For instance, in discretionary multitasking, autonomous driving is a domain in which multiple tasks exist, including a primary task associated with vigilance of the driving situation to prepare for a take-over and a secondary task such as reading a book, using a smart phone, or conversing with another person. Task switching is usually performed voluntarily in the autonomous driving domain. Therefore, in the proposed model, integrated accounts of task switching can be of significant assistance in developing the ACT-R model in complex multitasking domains such as autonomous driving.  
Conceptualization, H.O., R.M.; methodology, H.O., Y.Y.; software, H.O.; data curation, H.O.; validation, H.O.; formal analysis, H.O., Y.Y.; funding acquisition, R.M.; investigation, H.O., Y.Y., R.M.; writing--original draft, H.O.; writing--review and editing, H.O., Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant (19 TLRP-B131486-03) from the Transportation and Logistics R&D Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government, and from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A09084291).  
Ethical review and approval were waived for this study, due to the experiment was relatively short and there was no effect on the safety of the participants.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.