## 1 Introduction  
Automation enables a broad range of systems to reduce errors, improve work performance, expand human capabilities, and decrease effort and stress during operations [1]. It provides support for perceptual-cognitive and decision-making tasks, thus reducing the physiological effort and workload for the human operators as well [1, 2]. However, the benefits of automation may be realized and achieved only when the automation works as designed without any errors or malfunctions [3, 4]. If the automation fails, or the outcomes do not meet with operators' expectations, operators may perceive the automation as unreliable and inconsistent. Furthermore, excessive dependence on the availability and reliability of automation might interfere with skill acquisition without automation, which would make human operators misuse or disuse automation. This unreliability and inconsistency by Failed automation depends significantly on the levels of automation (LOA) and information processing stages. Previous research has defined the LOA by the amount of automation autonomy and human physical and cognitive activity [5; 6]. [PERSON] and [PERSON] [7] suggested 10 LOA as the basis of the classic human-machine task allocation principle: at a higher level, automation can execute decision-making tasks without the aid of human operators, at a lower level, it can execute an option within the operators' controls, and at a further lower level, it may be simply performed by the human operators. [PERSON] and [PERSON] [6] proposed that automation could be classified according to the information-processing stages: perceiving the status of the system variables or scanning displays (i.e., monitoring), formulation of task-processing plans (i.e., generating), deciding on an optimal plan (i.e., selecting), and the control actions at an interface (i.e., implementing). Elaborating on this classification, [PERSON] et. al. [4] presented a four-stage model of automation to support the design of human-automation interactions (HAI) in complex systems: information acquisition, information analysis, decision and action selection, and action implementation. This classification aids the formulation of specific function allocation schemes for automation. The combination of the LOA with information processing stages led to the development of a concept known as the degree of automation (DOA) [3]. Each function in a model or an information processing stage, either by the human or machine (or some combination thereof), is responsible for the effects of automation on the task performance. Under routine conditions or at higher LOA or DOA, human operators simply supervise the automation in an automation-driven mode, while in the case of automation failure, they are expected to override the situation control, which is referred to as a human-driven mode [8]. Generally, in an automation-driven mode, the automation enhances the overall system performance, while in a human-driven mode, it simply supports the system operation, or delivers signals or warnings to human operators to maximize the performance [9]. Broadly, automation yields a trade-off, in which better performance is exerted when all automation routinely works but increased dependence is induced, and worse performance may be produced when it fails [10]. This trade-off is analogous to the \"lumber jack effect\" in which \"the higher trees in the forest are, the farther they fall\" [3; 11]. The trade-off indicates that automation is compensated between the benefits of reliable automation and the expected costs of automation failures [12]. Not only does automated task performance but also workload and loss of situation awareness follow a similar trade-off. With a higher DOA, Routine automation progressively reduce the operator's workload, the automation enables the operator to engage in other concurrent tasks, but this frequently results in the loss of situation awareness (LSA).