I bring you a weekly bite-sized chunk of the science behind helicopter human factors and CRM in practice, simplifying the complex and distilling a helicopter related study into a summary of less than 500 words.
TITLE:
Modelling pilot decisions during One Engine Inoperative in maritime SAR helicopter hoist task: RPDM and PCM.
WHAT?
This study examined how experienced SAR helicopter pilots make decisions following an engine failure during a maritime hoist. It compared two Naturalistic Decision-Making models: the Recognition-Primed Decision Model (RPDM), which describes how experts rapidly recognise situations and select workable responses, and the Perceptual Cycle Model (PCM), which describes how understanding and action are continuously updated as new information becomes available.
WHERE?
Taiwan. Study of the National Airborne Service Corps.
WHEN?
Published in 2025 in The International Journal of Aerospace Psychology.
WHY?
SAR pilots can encounter complex emergencies for which checklists and conventional training do not provide a complete solution. Understanding the process by which experienced pilots recognise, interpret and respond to such situations may therefore be as important as judging the eventual decision.
The study used the ambiguous scenario of an engine failure during a maritime hoist to compare whether RPDM and PCM reveal different aspects of this decision process, and whether these insights could ultimately improve pilot training and reduce decision-making errors in high-risk operations.
HOW?
Nine SAR pilots took part in semi-structured interviews based on the Critical Decision Method, a technique used to uncover the thinking underlying expert decisions. They were presented with a hypothetical scenario involving an AS365 hovering at 100 ft over the sea, near maximum OGE hover weight and power, when one engine fails with a rescuer and casualty still suspended beneath the aircraft. The pilots knew about the engine failure in advance and described how they would assess and manage the situation. Their responses were then mapped against the decision making models to compare how effectively each represented the decision-making process described.
FINDINGS:
RPDM effectively represented pilots’ rapid, experience-based decision-making: recognising cues, establishing goals and mentally testing whether a plausible response would work. PCM provided a richer picture of how decisions evolved, showing pilots continually updating their mental model as information changed. The approaches were therefore complementary with RPDM explaining rapid recognition and PCM explaining continuing adaptation. A notable weakness emerged around cutting the hoist cable, where pilots’ knowledge was largely theoretical rather than experiential.
SO WHAT?
The study demonstrates why Naturalistic Decision Making is relevant to SAR. Under time pressure, experts do not systematically compare multiple options. Experience allows them to recognise a workable response and then adapt it if circumstances change.
Expert intuition described by these models depends upon having relevant experiences and schemas to draw upon. Although, simulation can help build the mental repertoire needed to recognise and manage rare, ambiguous emergencies, none of the participant pilots had experienced an OEI during an operational hoist.
A significant limitation is that this study modelled what pilots said they would do rather than observing what they actually did. Furthermore, participants knew the engine failure was coming, removing important characteristics of a real emergency such as startle, surprise and genuine time pressure. The authors therefore propose high-fidelity simulator research as the logical next step to test whether the decision processes identified here actually occur under realistic operational pressure.
REFERENCE:
Hung, C.-L., & Dai, M. D.-M. (2025). Modelling pilot decisions during one engine inoperative in maritime SAR helicopter hoist task: RPDM uand PCM. The International Journal of Aerospace Psychology, 35(3), 127–146. https://doi.org/10.1080/24721840.2025.2464115
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