2009
DOI: 10.1177/0018720809349709
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Identifying Black Swans in NextGen: Predicting Human Performance in Off-Nominal Conditions

Abstract: As new technology and procedures are envisioned for the future airspace, it is important to predict if these may compromise safety in terms of pilots' failing to notice unexpected events. Computational models such as N-SEEV support cost-effective means of making such predictions.

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Cited by 62 publications
(49 citation statements)
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References 27 publications
(31 reference statements)
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“…This is best represented when the indication of an automation failure is closer to the primary visual field (i.e., less in peripheral vision). This effect is predicted by the NSEEV model and supported by research findings (Yeh et al, 2003;Singh et al, 1997;Wickens, Hooey et al, 2009).  Event Type.…”
Section: Expectancy Salience and Event Type Effects On Detectionsupporting
confidence: 80%
See 1 more Smart Citation
“…This is best represented when the indication of an automation failure is closer to the primary visual field (i.e., less in peripheral vision). This effect is predicted by the NSEEV model and supported by research findings (Yeh et al, 2003;Singh et al, 1997;Wickens, Hooey et al, 2009).  Event Type.…”
Section: Expectancy Salience and Event Type Effects On Detectionsupporting
confidence: 80%
“…Inherent in the NSEEV (Noticing -SEEV) model (Wickens, 2014) of noticing changes, operators experience significantly degraded initial response to unexpected failures compared with more expected failures. Wickens, Hooey et al (2009) observed a reduction in detection rate from 78% to 50% produced by reducing the expectancy of the to-be-detected failure event. In research investigating operator interactions with automation, Molloy & Parasuraman (1996) observed complacency effects during the first failure condition.…”
Section: Expectancy Salience and Event Type Effects On Detectionmentioning
confidence: 92%
“…Noticing behaviour is dependent upon the bottom-up salience of the item of interest, both static (Itti & Koch, 2000) and dynamic salience (Yantis & Jonides, 1990); the effort to shift attention towards that item in terms of eccentricity from the current point of gaze (peripheral items are reduced in salience); the expectancy of the event occurring and the value or consequences/criticality of it being missed (top-down attentional settings). The model can be used to estimate both detection rates and response times in dynamic workspaces, and has been validated against empirical data on alert detection (Steelman et al, 2011;Wickens, Hooey, Gore, Sebok, & Koenicke, 2009). This modelling effort marks a significant departure from many previous applied studies that have relied upon operators' subjective reports to gauge notification saliency.…”
Section: Notifications In Atcmentioning
confidence: 99%
“…We consider cognitive mechanisms such as memory (e.g., Altmann & Trafton, 2002) and attention (e.g., Wickens, Hooey, Gore, Sebok, & Koenicke, 2009) that may impact the ability to efficiently switch between technological tools.…”
Section: A Taxonomy Of Team Task Switchingmentioning
confidence: 99%