Previous studies show that pilots sometimes make roll reversal errors (RREs) when responding to the aircraft bank angle shown on the attitude indicator (AI). This is suggestive of a perceptual ambiguity in the AI. In the current study, we investigated whether expectation contributes to such misperception. Twenty nonpilots performed tasks in a fixed-base flight simulator. Their expectation about the bank angle was manipulated with a flying task using outside view only. When flying at a bank angle, the outside view disappeared, a moving-horizon type AI was shown, and participants had to roll the wings level, trusting the AI. The AI often matched the previously flown turn. However, in some runs, it showed an opposite bank direction (Opposite condition), which was hypothesized to facilitate a misperception. In some other runs, it showed level flight (Level condition), which should not facilitate this. In a second session, participants rolled wings level without preceding flying task (Baseline condition). Participants made 11.2 times more RREs in the Opposite condition (75% error rate) compared to Baseline condition (6.7%), and 2.5 times more compared to the Level condition (30%). This indicates that RREs were in many cases caused by expectation-induced misperception of the AI bank angle.

Within current debates about the future impact of Artificial Intelligence (AI) on human society, roughly three different perspectives can be recognised: (1) the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; (2) the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; and (3) the collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: (a) a clear description for each of the three perspectives, along with their history and background; (b) an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and (c) a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society.

Background: Mnemonic-type startle and surprise procedures were previously proposed to help pilots cope with startle and surprise in-flight, but effects on performance after procedure execution have not yet been investigated. Objective: Thus, we tested the effectiveness a new mnemonic-type procedure in a moving-base simulator with a non-linear model of a small twin-propeller aircraft flown single-pilot. Method: An experimental group of twelve line pilots was trained to use a four-item procedure: 1. Calm down: take a deep breath, sit up straight and relax shoulders and hands. 2. Observe: call out the basic flight parameters. 3. Outline: formulate a hypothesis about the problem. 4. Lead: formulate and execute a plan of action. A control group of twelve line pilots received a control training. Next, all pilots performed four scenarios with startling and surprising events. Data were obtained on pilot performance, stress, procedure application and evaluation. Results: Application of the procedure in the test scenarios was high (90.0% full, 100.0% partly), and pilots evaluated the procedure positively (median: 4 on a 1–5 point scale). There was significantly superior decision-making in the experimental group, but immediate responses were significantly less optimal. Pilots sometimes applied the procedure at inappropriate moments. Conclusion: The results of the tested mnemonic-type procedure were promising. The procedure may benefit, however, from modifications to reduce complexity and to stimulate application at the appropriate moment.

Objective: This study tested whether simulator-based training of pilot responses to unexpected or novel events can be improved by including unpredictability and variability in training scenarios. Background: Current regulations allow for highly predictable and invariable training, which may not be sufficient to prepare pilots for unexpected or novel situations in-flight. Training for surprise will become mandatory in the near future. Method: Using an aircraft model largely unfamiliar to the participants, one group of 10 pilots (the unpredictable and variable [U/V] group) practiced responses to controllability issues in a relatively U/V manner. A control group of another 10 pilots practiced the same failures in a highly predictable and invariable manner. After the practice, performance of all pilots was tested in a surprise scenario, in which the pilots had to apply the learned knowledge. To control for surprise habituation and familiarization with the controls, two control tests were included. Results: Whereas the U/V group required more time than the control group to identify failures during the practice, the results indicated superior understanding and performance in the U/V group as compared to the control group in the surprise test. There were no significant differences between the groups in surprise or performance in the control tests. Conclusion: Given the results, we conclude that organizing pilot training in a more U/V way improves transfer of training to unexpected situations in-flight. Application: The outcomes suggest that the inclusion of U/V simulator training scenarios is important when training pilots for unexpected situations.

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.