## 4 Discussions and Conclusions  
The purpose of this study is not to differentiate the differences in response performance and monitoring behavior between different failure types; rather it is to quantify these differences and consider the level of operational proficiency in order to assess the impact of failure types on response performance and monitoring behavior in different proficiency scenarios. It is hoped that researchers will pay attention to these differences, and we encourage them to consciously consider the proficiency levels of their participants and the types of system failures implemented in their studies. Previous studies have found that individuals have faster response times to expected events compared to unexpected events [28]. This is because expected events can be anticipated and prepared for, whereas unexpected events cannot. However, some of the previous research comparing the takeover performance between system-malfunction failures and system-limit failures did not find significant differences [16]. Another study indicated that there were differences between the two, but the differences in takeover time were minimal (e.g., less than 1 s) [15]. In contrast, the quantitative analysis of response time differences in this study for the two types of failures revealed substantial disparities. Across proficiency levels 1 to 4, the response time for system-malfunction failures was 3.644, 2.471, 2.604, and 4.545 times longer than that for system-limit failures. Looking at the absolute values, at proficiency level 1, the response times for resolving system-malfunction failures and system-limit failures were 39.194 s and 10.756 s, respectively; at proficiency level 4, they were 14.988 s and 3.298 s, respectively. Therefore, this study confirms significant and substantial differences in response times between the two types of failures, highlighting the importance of not disregarding the influence of failure types on response performance. Furthermore, compared to driving a vehicle, the experimental task in this study simulated a complex flight mission. The complexity of the flight task itself and the manifestation of failures may contribute to longer response times and larger time differences. Therefore, it is possible that as tasks and failures become more complex, response times become longer, and the differences between system-malfunction failures and system-limit failures increase. This aspect needs to be further investigated in future research. When unexpected events occur during flight, experienced pilots require shorter response times to make appropriate operations compared to novices. This has been confirmed by numerous studies [29; 30; 31]. However, these previous research articles usually focused on predictable situations (similar as system-limit failures), neglecting the consideration of unpredictable circumstances and the performance differences caused by different types of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proficiency Level** & **Failure Type** & **Saccade Rate** & **SD** \\ \hline \multirow{3}{*}{1. Inexperienced} & Malfun & 2.403 & 1.176 \\  & Limit & 2.267 & 1.401 \\  & Malfun & 2.198 & 0.842 \\  & Limit & 2.990 & 1.901 \\  & Malfun & 2.313 & 1.072 \\  & Limit & 2.869 & 1.436 \\  & Malfun & 2.331 & 1.239 \\  & Limit & 2.372 & 1.465 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The saccade rate for system-malfunction and system-limit failures at four proficiency levels. failures at various proficiency levels. In addition to confirming the results consistent with previous studies, which indicate that participants' response times for resolving failures significantly decrease as proficiency increases, this study further reveals that the effect of proficiency levels on reducing response time varies across different types of failures. In the case of system-malfunction failure, increasing proficiency has a significant effect on reducing response time at lower proficiency levels, but its effect becomes less apparent at higher proficiency levels.