Testing and implementing cockpit alerting systems

Pritchett, Amy R.; Vándor, Balázs; Edwards, Kimberly

doi:10.1016/s0951-8320(01)00094-1

Cited by 35 publications

(19 citation statements)

References 18 publications

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“…Radiologists, for example, are known to be concerned with explaining why each prompt is present [37,41]. Also, in aviation, pilots using TCAS (Traffic Collision Avoidance System) are strictly instructed to regard all automated messages as genuine alerts demanding an immediate, high-priority response [42]. Processing false prompts demands time and cognitive resources, and thus can lead to "Time Pressure" (node 9) and "Cognitive Overload" (node 5: presence of confusion that does not allow the operator to process information properly).…”

Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 99%

Why Are People’s Decisions Sometimes Worse with Computer Support?

Alberdi

Strigini

Povyakalo

et al. 2009

Lecture Notes in Computer Science

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract. In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action. For instance, training or procedural changes are favored responses, but they do not address all causes of apparently "over-reliant" behaviour. We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature. Permanent repository link

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Section: A Case Study: Computer Aided Detection (Cad) For Mammographymentioning

confidence: 99%

Why Are People’s Decisions Sometimes Worse with Computer Support?

Alberdi

Strigini

Povyakalo

et al. 2009

Lecture Notes in Computer Science

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“…Once a fault has been perceived (whether the perception is right or wrong), the remainder of the procedure may be stereotyped as untrustworthy instead of each step being judged for its individual merits. This effect is similar to the effects of automation faults on operator trust and reliance (Lee & Moray, 1994;Muir, 1987) and of false alarms on operator conformance to subsequent alerting system commands (Hasse, 1992;Pritchett, Vándor, & Edwards, 1999).…”

Section: Mitigating Intentional Noncompliancementioning

confidence: 61%

A Review and Reappraisal of Task Guidance: Aiding Workers in Procedure Following

Ockerman¹,

Pritchett

2000

International Journal of Cognitive Ergonomics

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Procedures can greatly benefit workers at tasks such as inspection, maintenance, and assembly. Procedures may serve as a guideline for expert workers or they may provide a list of directives to be followed exactly; either way, procedures serve to structure a task, to aid worker memory, and to guarantee consistency and safety. Light, inexpensive electronics may allow for the development of task guidance systems to further help workers by presenting procedures and associated information about the task. This article reviews the current state of knowledge about the development of task guidance systems, and highlights their potential value in a variety of domains. First, the characteristics of procedural tasks are discussed, as a basis for a discussion on the benefits of procedure following that task guidance systems can support, and potential problems in procedure following that task guidance systems can mitigate. Then, current research results in task guidance systems are summarized. Finally, a discussion is given on the contextual information that a task guidance system may need to provide.

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“…A second objective, also related to reducing uncertainty in the cockpit, is the objective not to have conflicting alarms -also known as dissonance [37], [38]. This objective can unfortunately be derived from a lessons learned from a tragic flight accident.…”

Section: Flight Definitionmentioning

confidence: 99%

A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics

Wheeler

Kurtoglu

Poll

2009

Volume 2: 29th Computers and Information in Engineering Conference, Parts a and B

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One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis. Using this mapping, the gaps between user goals and the metrics associated with diagnostics and prognostics are identified and presented with a collection of lessons learned from previous studies that include both industrial and military aerospace applications.*

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Testing and implementing cockpit alerting systems

Cited by 35 publications

References 18 publications

Why Are People’s Decisions Sometimes Worse with Computer Support?

Why Are People’s Decisions Sometimes Worse with Computer Support?

A Review and Reappraisal of Task Guidance: Aiding Workers in Procedure Following

A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics

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