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by Song, Jingyan. / "January 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 110-118). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

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Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2008. / Committee Chair: Fisk, Arthur; Committee Member: Corso, Gregory; Committee Member: Rogers, Wendy.

Automation provides the opportunity for many tasks to be done more effectively and with greater safety. However, these benefits are unlikely to be attained if an automated system is designed without the human user in mind. Many characteristics of the human and automation, such as trust and reliability, have been rigorously examined in the literature in an attempt to move towards a comprehensive understanding of the interaction between human and machine. However, workload has primarily been examined solely as an outcome variable, rather than as a predictor of compliance, reliance, and performance. This study was designed to gain a deeper understanding of whether workload experienced by human operators influences compliance with and reliance on an automated warehouse management system, as well to assess whether age-related differences exist in this interaction. As workload increased, performance on the Receiving Packages task decreased among younger and older adults. Although younger adults also experienced a negative effect of workload on Dispatching Trucks performance, older adults did not demonstrate a significant effect. The compliance data showed that as workload increased, younger adults complied with the automation to a greater degree, and this was true regardless of whether the automation was correct or incorrect. Older adults did not demonstrate a reliable effect of workload on compliance behavior. Regarding reliance behavior, as workload increased, reliance on the automation increased, but this effect was only observed among older adults. Again, this was true regardless of whether the automation as correct or incorrect. The finding that individuals may be more likely to comply with or rely on faulty automation if they are in high workload state compared to a low workload state suggests that an operator's ability to detect automation errors may be compromised in high workload situations. Overall, younger adults outperformed older adults on the task. Additionally, older adults complied with the system more than younger adults when the system erred, which may have contributed to their poorer performance. When older adults verified the instructions given by the automation, they spent longer doing so than younger adults, suggesting that older adults may experience a greater cost of verification. Further, older adults reported higher workload and greater trust in the system than younger adults, but both age groups perceived the reliability of the system quite accurately. Understanding how workload and age influence automation use has implications for the way in which individuals are trained to interact with complex systems, as well as the situations in which automation implementation is determined to be appropriate.

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As automated technologies continue to advance, they will be perceived more as collaborative team members and less as simply helpful machines. Expectations of the likely performance of others play an important role in how their actual performance is judged (Stephan, 1985). Although user expectations have been expounded as important for human-automation interaction, this factor has not been systematically investigated. The purpose of the current study was to examine the effect older and younger adults expectations of likely automation performance have on human-automation interaction. In addition, this study investigated the effect of different automation errors (false alarms and misses) on dependence, reliance, compliance, and trust in an automated system. Findings suggest that expectancy effects are relatively short lived, significantly affecting reliance and compliance only through the first experimental block. The effects of type of automation error indicate that participants in a false alarm condition increase reliance and decrease compliance while participants in a miss condition do not change their behavior. The results are important because expectancies must be considered when designing training for human-automation interaction. In addition, understanding the effects of type of automation errors is crucial for the design of automated systems. For example, if the automation is designed for diverse and dynamic environments where automation performance may fluctuate, then a deeper understanding of automation functioning may be needed by users.

As the tasks humans perform become more complicated and the technology manufactured to support those tasks becomes more adaptive, the relationship between humans and automation transforms into a collaborative system. In this system each member depends on the input of the other to reach a predetermined goal beneficial to both parties. Studying the human/automation dynamic as a social team provides a new set of variables affecting performance previously unstudied by automation researchers. One such variable is the shared mental model (Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000). This study examined the relationship between mental model quality and collaborative system performance within the domain of a navigation task. Participants navigated through a simulated city with the help of a navigational system performing at two levels of accuracy; 70% and 100%. Participants with robust mental models of the task environment identified automation errors when they occurred and optimally navigated to destinations. Conversely, users with vague mental models were less likely to identify automation errors, and chose inefficient routes to destinations. Thus, mental model quality proved to be an efficient predictor of navigation performance. Additionally, participants with no mental model performed as well as participants with vague mental models. The difference in performance was the number and type of errors committed. This research is important as it supports previous assertions that humans and automated systems can work as teammates and perform teamwork (Nass, Fog, & Moon, 2000). Thus, other variables found to impact human/human team performance might also affect human/automation team performance just as this study explored the effects of a primarily human/human team performance variable, the mental model. Additionally, this research suggests that a training program creating a weak, inaccurate, or incomplete mental model in the user is equivalent to no training program in terms of performance. Finally, through a qualitative model, this study proposes mental model quality affects the constructs of user self confidence and trust in automation. These two constructs are thought to ultimately determine automation usage (Lee & Moray, 1994). To validate the model a follow on study is proposed to measure automation usage as mental model quality changes.

Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2008. / Committee Chair: Arthur D. Fisk; Committee Member: Gregory M. Corso; Committee Member: Wendy A. Rogers.

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