Interaction of Automation Visibility and Information Quality in Flight Deck Information Automation

Dorneich, Michael C.; Dudley, Rachel; Letsu-Dake, Emmanuel; Rogers, William H.; Whitlow, Stephen D.; Dillard, Michael; Nelson, Erik T.

doi:10.1109/thms.2017.2717939

Cited by 25 publications

(12 citation statements)

References 48 publications

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“…Simply put, transparency affords one agent with an understanding of past, present and future actions of another agent. Theoretical (Lee & See, 2004) and experimental studies (Chen, Barnes, Selkowitz, & Stowers, 2016;Dorneich et al, 2017;Lyons et al, 2017;Wang, Pynadath, & Hill, 2016) have consistently agreed on the significance of automation transparency in engendering human trust in automation. Moreover, many researchers (Chen et al, 2016(Chen et al, , 2018(Chen et al, , 2014Lyons, 2013;Wang et al, 2016) highlighted the desirable effects of transparency in facilitating shared understanding and guiding humans' proper reliance on automation.…”

Section: Introductionmentioning

confidence: 91%

“…Several investigations explored how different factors contribute to the usefulness of transparency. These factors include: information depth (Sadler et al, 2016;Wang et al, 2016), quality (Dorneich et al, 2017), and format (Desai, Kaniarasu, Medvedev, Steinfeld, & Yanco, 2013); as well as the degree of situational awareness afforded by transparency (Chen et al, 2014). However, research on the individual factors that affect human's processing of the information provided by a transparent automation has received less attention.…”

Section: Introductionmentioning

confidence: 99%

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Investigating Gender Differences in Human Interactions with a Transparent Swarm

Hussein

Elsawah

Abbass

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Research suggests that different operationalisations of automation transparency can influence team collaboration and performance. Yet, little is known about how gender differences can affect humans’ ability to process the information provided by their automation teammate when the automation is transparent. The significance of this research question stems from the fact that the broader areas of judgment and decision making show that females and males deploy different information processing strategies. The aim of this paper is to explore whether gender differences exist in the way people interact with a transparent swarm. We conducted a user study in which 33 subjects (15 males and 18 females) interacted with a simulated swarm under two conditions: with and without transparency. While no significant differences were detected under the control condition, results indicate that when transparency was added, males were able to utilise transparency early on, while females needed significantly more time to benefit from it. Besides, under the transparency condition, females exhibited significantly longer response times than males. However, as time progresses and towards the last time window of the experiment, females could achieve slightly higher decision accuracy than males. These findings stress the need to consider gender differences when designing transparent human-machine interaction and its training protocols.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Investigating Gender Differences in Human Interactions with a Transparent Swarm

Hussein

Elsawah

Abbass

2019

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

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“…For instance, “decision selection” requires cognitive skills, including categorizing and prioritizing information quickly and efficiently, searching and accessing information, and validating information quality (Letsu-Dake, Rogers, Dorneich, & DeMers, 2012). As automation takes over these types of functions, the amount of visibility into the reasoning of the automation, as well as the quality of information used by the automation, is important as humans monitor and assess the outputs of the automation (Dorneich et al, 2017). Thus, the designer of a CA system must weigh the benefits and costs when deciding how to allocate functions and what level of collaboration will be integrated to the human-CA system.…”

Section: Related Workmentioning

confidence: 99%

Development of Design Requirements for a Cognitive Assistant in Space Missions Beyond Low Earth Orbit

Tokadl

Dorneich

2017

Journal of Cognitive Engineering and Decision Making

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This study describes the development of requirements for a cognitive assistant (CA) for use onboard a space vehicle/station. For missions beyond low Earth orbit (LEO), delayed communication will limit mission control's ability to support the space crew in real time. During off-nominal situations, where no procedures have been developed prior to missions, crews must develop responses in real time and may increasingly rely on automation. A systematic approach was used to model the domain knowledge of the collaborative decision-making process of current space operations, extrapolate to missions beyond LEO, and develop the design requirements for a CA. Document analysis and interviews were conducted to create an abstraction hierarchy and a decision-action diagram of the cognitive functions currently performed by space crew, mission control, and onboard automation. These domain models were extrapolated to missions beyond LEO by identifying the breakpoints where current decision-making processes would break down due to increased communication delay between mission control and the space crew. Design requirements were identified for future CA systems that offer real-time decision-making support to mitigate the negative effect of limited support in off-nominal situations. The approach developed for this research can be generalized to identify the design requirements for future support systems in domains beyond space operations.

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“…An aerospace manufacture is currently working with Human Factors researchers to design and develop an engine condition monitoring system, similar to that detailed in the NTSB (2010) report, to aid pilot's decisions when faced with engine failure events. Before implementing a decision assistant, it is essential that the scope of the system is reviewed to understand its integration with current decision‐making procedures (Banks, Allison, et al, 2020; Dorneich et al, 2017; Mosier & Manzey, 2019; Mosier & Skitka, 1996; Parnell et al, 2019). The pilot's decision making within such a scenario also needs to be fully understood to design aids that can best inform them.…”

Section: Introductionmentioning

confidence: 99%

Pilot decision‐making during a dual engine failure on take‐off: Insights from three different decision‐making models

Parnell

Wynne

Plant

et al. 2021

Hum Ftrs & Erg Mfg Svc

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Commercial airline pilots are required to make efficient, justifiable, and safety-critical decisions when faced with adverse events such as engine failures. Although these are rare events, the consequences are severe, and the pilot response is critical. This paper reviews pilot decision-making when faced with a dual engine failure on takeoff using three different decision models; the Recognition Primed Decision Model, Decision Ladders and the Perceptual Cycle Model. In-depth interviews with eight experienced airline pilots were conducted to capture their decision-making processes in response to a dual engine failure on take-off event. The analysis of these interviews using the three different decision models provide recommendations for a proposed decision assistant. The different decision models are discussed in relation to the insight they can bring to developing a future decision assistant tool within the flight deck of commercial aircraft.

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Interaction of Automation Visibility and Information Quality in Flight Deck Information Automation

Cited by 25 publications

References 48 publications

Investigating Gender Differences in Human Interactions with a Transparent Swarm

Investigating Gender Differences in Human Interactions with a Transparent Swarm

Development of Design Requirements for a Cognitive Assistant in Space Missions Beyond Low Earth Orbit

Pilot decision‐making during a dual engine failure on take‐off: Insights from three different decision‐making models

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