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• W2397015047 abstract "Experimental Investigation of Simultaneous Use of Automation and Alert Systems Akihiro Maehigashi (mhigashi@cog.human.nagoya-u.ac.jp) Institute of Innovation for Future Society, Nagoya University, Japan Kazuhisa Miwa (miwa@is.nagoya-u.ac.jp) Hitoshi Terai (terai@is.nagoya-u.ac.jp) Graduate School of Information Science, Nagoya University, Japan Kazuaki Kojima (kojima@lt-lab.teikyo-u.ac.jp) Learning Technology Laboratory, Teikyo University, Japan Junya Morita (j-morita@cmc.ss.is.nagoya-u.ac.jp) Institute of Innovation for Future Society, Nagoya University, Japan Abstract In this study, we experimentally investigated the effects of the interaction between two individual systems, an automation system that conducts tasks and an alert system that monitors automation performance and alerts users to automation fail- ures. The experimental results showed that when users used automation and alert systems together, when the alert system missed automation failures, the participants lowered their trust not only in the alert system but also in the automation system. It means that the participants confused trust in the automation system with that in the alert system. Moreover, when the par- ticipants highly trusted the automation system, they slowly re- sponded to a true alert from the alert system. These results were discussed on the basis of the theory shown in previous studies. Keywords: Automation system; Alert system; Trust; Miss; False alarm Introduction In recent years, progress in technology provides many oppor- tunities for people to use automation systems such as auto- driving systems and autopilot systems. An automation system is a technology that autonomously conducts a task on behalf of humans (Parasuraman & Riley, 1997). Users can reduce workload by using automation systems. The problem with us- ing automation systems is that they are not always perfect and cause failures that might lead to fatal accidents. Therefore, when using an automation system, users need to monitor au- tomation performance, and if automation failures occur, they have to handle the failures (Parasuraman & Manzey, 2010). An alert system is a technology that monitors the automa- tion performance for possible failures on behalf of users and alerts users when automation failures occur (e.g., Dixon & Wickens, 2006; Wickens & Colcombe, 2007). The users can reduce monitoring loads and suppress missing automation failures by using an alert system. However, the problem with using alert systems is that they are not always perfect, and similar to automation systems, they can cause failures. There are two types of alert failures: miss and false alarm (FA). Miss is failing to alert users when an automation failure happens, and FA is alerting users when no automation failure happens (Sorkin & Woods, 1985). Therefore, the problem with using automation and alert systems together is that when one of the systems or both systems cause failures, users have difficul- ties understanding what is actually happening and finding the cause of the failures. Such a problem that occurs when people use complex arti- facts has been pointed out in the field of cognitive science and cognitive engineering. Rasmussen (1986) indicated that as ar- tifacts became complex according to progress in technology, the role of the artifacts became large in the interaction be- tween humans and the artifacts, and the connection between humans and the objects of tasks became indirect. Moreover, Norman (1988) indicated that in a situation in which such an indirect connection between humans and the objects of tasks occurs, there are difficulties in users’ execution of actions and evaluation of the results of the actions; therefore, the interface between users and artifacts should be designed to allow users to appropriately execute actions and evaluate the results of these actions. In the previous studies of automation and alert systems, it has been found that when users use automation and alert sys- tems, which have complex internal processing mechanisms, they make presumptions as to what is actually happening in- side of the systems and to the objects of tasks by the use of trust as an evaluation parameter. A brief summary of the find- ings of previous studies is as follows: • Preceding Finding (PF) I: Automation performance deter- mines users’ trust in the automation system. In particu- lar, automation failures lower users’ trust in the automation system (Lee & Moray, 1992; Parasuraman & Riley, 1997). • PF II: In the previous studies about alert systems, because the main focus was investigating the effects of missed alerts and FAs on human behavior, those effects on users’ subjec- tive trust in the alert systems were not directly measured. However, the results of the experiments predict that missed alerts and FAs lower users’ trust in the systems (e.g., Dixon & Wickens, 2006; Wickens & Colcombe, 2007). • PF III: Users’ trust in an alert system influences their re- sponse to a true alert (TA), alerting users when an automa- tion failure happens. Particularly, FAs lower users’ trust in the alert system and cause delayed responses to TAs (Cry-wolf effect) (e.g., Dixon & Wickens, 2006; Wickens & Colcombe, 2007). In the previous studies, automation and alert systems were treated as two different systems and were individually investi- gated in terms of the points written above. In contrast with the previous studies, in this study, we investigated the interaction between the two different systems. Figure 1 is a conceptual representation that summarizes the findings of the previous studies and the research questions in this study. The research questions in this study are as follows:" @default.
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