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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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Driving is one of the most complex and dangerous tasks that is regularly performed by most adults. Whereas most research examines performance in novel situations, most everyday driving occurs in highly familiar settings, such as our daily commute. Here we compared drivers’ hazard identification on familiar roads with similar but unfamiliar roads, for five road types: city streets, suburban streets, urban roads, mountain roads, and motorways. Participants were 45 experienced drivers with on average 17.6 years driving experience ( SD = 5.2), and 32 novices with on average 6.2 months solo driving ( SD = 3.5). Experienced drivers identified more hazards than novices, regardless of road type, but the magnitude of the effect was surprisingly small. The overall effect of location familiarity on hazard identification was not statistically significant, but there were significant effects of road type and significant interactions between familiarity and road type, which suggests researchers should be cautious when generalizing results obtained from one road context to another.

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