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.

Wave-variable transformation is a means to maintain stability of haptic teleoperation in the presence of communication time delays. Its drawback is that it affects haptic perception of remote properties and thereby degrades transparency. This paper studies the effect of wave-variable transformation on human haptic perception. Based on a framework of haptic perception developed in previous work, we systematically investigated how the wave variable affects human perception of damping, mass and stiffness properties of an arbitrary linear environment. Both the original wave-variable approach and the generalized wave-variable approach are investigated. Results show how both approaches change human perception of all three mechanical properties of the environment, and how these changes vary with both excitation frequency and time delay. The generalized wave-variable approach on the whole outperforms the original in terms of rendering mass and stiffness, but not always for rendering damping. Results also show that human perception of the dynamics rendered by both approaches is similar to that of the original environment only when time delays are small. As the time delay increases, evaluating the mechanical properties can become very difficult for a human operator if the interaction with the environment is not static.

Objective We tested whether a procedure in a hexapod simulator can cause incorrect assumptions of the bank angle (i.e., the “leans”) in airline pilots as well as incorrect interpretations of the attitude indicator (AI). Background The effect of the leans on interpretation errors has previously been demonstrated in nonpilots. In-flight, incorrect assumptions can arise due to misleading roll cues (spatial disorientation). Method Pilots ( n = 18) performed 36 runs, in which they were asked to roll to wings level using only the AI. They received roll cues before the AI was shown, which matched with the AI bank angle direction in most runs, but which were toward the opposite direction in a leans-opposite condition (four runs). In a baseline condition (four runs), they received no roll cues. To test whether pilots responded to the AI, the AI sometimes showed wings level following roll cues in a leans-level condition (four runs). Results Overall, pilots made significantly more errors in the leans-opposite (19.4%) compared to the baseline (6.9%) or leans-level condition (0.0%). There was a pronounced learning effect in the leans-opposite condition, as 38.9% of pilots made an error in the first exposure to this condition. Experience (i.e., flight hours) had no significant effects. Conclusion The leans procedure was effective in inducing AI misinterpretations and control input errors in pilots. Application The procedure can be used in spatial disorientation demonstrations. The results underline the importance of unambiguous displays that should be able to quickly correct incorrect assumptions due to spatial disorientation.

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