## 4 Discussion  
### Effects of Automation on Task Performance  
The results showed that the automation levels marginally affected both performance measures while automation statuses moderately affected both task completion time and accuracy of proofreading tasks. Comparing task performance in Routine status, SA ratios (task accuracy) were more increased than ST ratios (task completion time) by a high level of automation (see Figures 2 and 3). Although this comparison needs to consider the ceiling and flooring effects, the sub-functions of the automation task may lead to different performance outcomes. The task used in this study comprises cognitive functions such as detecting a typographical error, correcting the error, and checking the spelling, and physical functions such as typing or writing the corrected word. At Low automation, the detecting function was automated, while at High automation, both detecting and correcting functions were  
Figure 5: NASA TLX subscale scores by levels of automation.  
automated. We observed that as the automation level increased, more cognitive functions than the physical functions are automated. Cognitive functions are vulnerable to the mistakes that are relevant to task accuracy, while physical functions easily commit errors that worsen the task completion time such as lapses, and slips [27; 28]. Thus, we could infer that SA ratios are more enhanced than ST rations by the Routine automation.  
Contrastingly, the almost \"flat\" pattern of the performances at different automation levels was observed in the Failed status. As shown in Figures 2 and 3, the average task performance in both Low and High levels of Failed automation was not significantly different. These performance patterns are closely associated with the lumber jack hypothesis suggested by [PERSON] et al. [3]. According to the hypothesis, the performance follows a \"flat\" function up to a certain critical point regardless of the automation level in Failed automation. Therefore, the task performance in this range of Failed automation would not be affected by malfunctioned automation or unexpected automation failures, and the users could maintain their performance despite they acknowledge that the automation did not work properly.  
This flat performance in Failed automation can be upheld by the skill, rule, and knowledge (SRK)-based classification. The SRK classification provides a useful framework to distinguish human behaviors or tasks based on the type of information processing demands and the different states of the constraints in working environments [27; 29]. According to the SRK classification, human behaviors can be categorized into cognitive (rule/knowledge-based) and physical (skill-based) tasks [27]. Since automation is defined as the technology or the system to minimize human assistance, and it reduces information processing demands or difficult physical activities [30], rule/knowledge-based complex cognitive tasks are transformed into simple physical skill-based or rule-based tasks in Routine automation. However, Failed automation requires cognitive demanding rule/knowledge-based tasks (e.g., searching for an error). Given tasks in this study, the dexterity, knowledge, and mental resource required to complete the tasks are not complex enough to be knowledge-based tasks, and the conscious control of action with low cognitive demands (i.e., reading capability to choose the right words) and routine practice to apply the simple rule (i.e., a basic knowledge of grammar) are enough. Thus, participants can maintain their performance even in Failed automation, and this characteristic of the subtask in Failed automation results in a \"flat\" task performance. If the tasks depend on a higher level of automation, they will consist of more rule/knowledge-based behaviors and malfunctioned automation causes a severe decrease in the task performance.