Surprise and unexpectedness in flying: Database reviews and analyses

Kochan, Janeen A.; Breiter, Eyal G.; Jentsch, Florian

doi:10.1037/e577272012-013

Cited by 7 publications

(13 citation statements)

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“…In several recent flight safety events, such as those involving loss of control during flight, the unexpectedness of the situation is thought to have induced a “startle factor,” complicating the crew’s troubleshooting ( Belcastro & Foster, 2010 ; Bürki-Cohen, 2010 ; Kochan, Breiter, & Jentsch 2004 ; Martin, Murray, Bates, & Lee, 2016 ; Shappell et al, 2007 ). In response to these events, new regulations include recommendations to incorporate startle and surprise in training programs to prepare flight crews for unexpected events ( European Aviation Safety Agency [EASA], 2015 ; Federal Aviation Administration [FAA], 2015 ; International Civil Aviation Organisation [ICAO], 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Dealing With Unexpected Events on the Flight Deck: A Conceptual Model of Startle and Surprise

Landman

Groen²,

Paassen

et al. 2017

Hum Factors

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Objective:A conceptual model is proposed in order to explain pilot performance in surprising and startling situations.Background:Today’s debate around loss of control following in-flight events and the implementation of upset prevention and recovery training has highlighted the importance of pilots’ ability to deal with unexpected events. Unexpected events, such as technical malfunctions or automation surprises, potentially induce a “startle factor” that may significantly impair performance.Method:Literature on surprise, startle, resilience, and decision making is reviewed, and findings are combined into a conceptual model. A number of recent flight incident and accident cases are then used to illustrate elements of the model.Results:Pilot perception and actions are conceptualized as being guided by “frames,” or mental knowledge structures that were previously learned. Performance issues in unexpected situations can often be traced back to insufficient adaptation of one’s frame to the situation. It is argued that such sensemaking or reframing processes are especially vulnerable to issues caused by startle or acute stress.Conclusion:Interventions should focus on (a) increasing the supply and quality of pilot frames (e.g., though practicing a variety of situations), (b) increasing pilot reframing skills (e.g., through the use of unpredictability in training scenarios), and (c) improving pilot metacognitive skills, so that inappropriate automatic responses to startle and surprise can be avoided.Application:The model can be used to explain pilot behavior in accident cases, to design experiments and training simulations, to teach pilots metacognitive skills, and to identify intervention methods.

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Section: Introductionmentioning

confidence: 99%

Dealing With Unexpected Events on the Flight Deck: A Conceptual Model of Startle and Surprise

Landman

Groen²,

Paassen

et al. 2017

Hum Factors

View full text Add to dashboard Cite

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“…Similarly, a startle could occur in the absence of surprise, for instance when a stimulus is loud and scary but not unexpected. Surprise such as automation surprises seem to occur quite frequently in today's operational practice, although it remains inconsequential in most cases (de Boer & Hurts, 2017;Kochan, Breiter, & Jentsch, 2004).…”

mentioning

confidence: 99%

The Influence of Surprise on Upset Recovery Performance in Airline Pilots

Landman

Groen²,

Paassen

et al. 2017

The International Journal of Aerospace Psychology

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Objective: The aim of this study was to test if performance of airline pilots, in performing an aerodynamic stall recovery procedure, decreases when they are surprised, compared to when they anticipate a stall event.Background: New flight-safety regulations for commercial aviation recommend the introduction of surprise and startle in upset prevention and recovery training. This calls for more evidence on the effects of surprise on pilot performance, as well as methods to effectively induce surprise in training simulators.Method: The study took place in a motion-base simulator with a poststall aerodynamic model. Using a within-subjects design, the recovery performance of 20 pilots was tested in 2 conditions: 1 anticipated condition, and 1 surprise condition. In addition to flight parameters, subjective and physiological data relating to surprise and startle were measured.Results: Pilots had significantly more difficulties with adhering to the recovery procedure in the surprise condition compared to the anticipation condition. The subjective and physiological measures confirmed that the manipulation mainly increased surprise, and to a lesser extent also startle.Conclusion: The results suggest that pilots have more difficulty in managing an upset situation (i.e., an aerodynamic stall) when this situation is presented unexpectedly, underlining that upset prevention and recovery training should include elements of surprise.

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“…Unexpected event. For creating an unexpected event to understand the effect of correspondence bias, we followed the principles about the prior knowledge of the unexpected event made by Kochan, Breiter, and Jentsch (2004). In the study, a 250 Hz tone was played continually for four seconds during the surprise condition at trial two and trial four via…”

Section: Equipment and Materialsmentioning

confidence: 99%

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

Hsiung

Chiou

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after a surprise, or unexpected event. These biases further influence people’s trust in their partners, including machine partners (Muir, 1987; Madhavan & Wiegmann, 2004). Advances in robotics have allowed for robots to partner with people at work and be treated socially (Young, Hawkins, Sharlin & Igarashi, 2009). However, these advances may interfere with a person’s appropriate calibration of trust in robots (Parasuraman & Miller, 2004). A better understanding of attribution biases in the wake of an unexpected event may shed light on how trust develops in a robot partner. This study was built on a human coordination example to serve as a reference for future human-robot interactions. We posit that attribution biases lead people to blame their partner after experiencing a negative performance outcome, thus lowering their trust in the partner. Sixty participants (30 pairs) were tasked to coordinate with an unfamiliar human partner, to lift a 17.5 lb. box containing a 200ml cup of water filled to the brim, from the floor to a table, as quickly as possible without spilling water. Before the task, participants were told that the pair with the best performance would be rewarded; however, all pairs were told they did not achieve this. Participant pairs were randomly assigned to a surprise condition during which they heard a 250 Hz warning tone, or a baseline condition with no warning tone. Participants in both conditions were told to pause the task as quickly as possible if the warning tone was present. It was unknown to participants when or if a warning tone would occur. To assess participants’ trust in their partner, Muir’s (1987) trust questionnaire was administered twice, once after introducing the task to participants, and again after the coordination task was completed. To capture blame assignment, a scale based on Kim and Hinds (2006) was administered after participants were told they did not achieve the best performance. Results indicate participants were less likely to blame their partners for the negative outcome, compared to blaming themselves or the warning tone itself (in the surprise condition). Next, surprisingly, in the surprise condition, instead of experiencing a decrease of trust in a partner after the negative outcome, there was a significant increase in trust in their partners. No significant difference in trust was found in the baseline condition. Finally, results also indicate that initial trust in a partner is a significant predictor for how people assign blame. In general, the effects of attribution biases were not observed in the present study. Friendliness may be a factor in people’s assignment of blame; although participants were unfamiliar with one another, all participants were students at the same university. Second, shared experience during the surprise condition, including the chance to assess their partner’s behaviors in response to the warning tone, may have been a catalyst for increased trust in a partner. It is important to note that although physical differences between participants were not evaluated in this study, height may be a potential confounding factor in this task. These findings enlighten our understanding of physical human-robot coordination scenarios and trust in a partner.

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Surprise and unexpectedness in flying: Database reviews and analyses

Cited by 7 publications

References 0 publications

Dealing With Unexpected Events on the Flight Deck: A Conceptual Model of Startle and Surprise

Dealing With Unexpected Events on the Flight Deck: A Conceptual Model of Startle and Surprise

The Influence of Surprise on Upset Recovery Performance in Airline Pilots

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

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