The ability to execute multiple flight tasks simultaneously is a basic requirement for safe aircraft operation. To the present time, there is no consensus about the degree to which simultaneous task execution is actually possible without performance decrements. The flexibility perspective on multitasking explains how cognitive control enables task sets to be flexibly activated and shielded from interference. However, cognitive control is subject to the stability-flexibility dilemma. This dilemma describes the conflicting demands on cognitive control that influence goal-directed behaviour in multitasking situations. On the one hand, cognitive stability has the advantage of minimizing task interference, while not facilitating flexible goal updating. On the other hand, cognitive flexibility allows for constant background monitoring and facilitates task switching. In addition, it has been demonstrated that overlearned action sequences reduce multitasking costs, but are also accompanied by mitigated behavioural flexibility. However, behavioural flexibility is particularly necessary in novel and complex flight scenarios to ensure a pilot’s rapid operational readiness. This issue raises two questions: How does the stability-flexibility-dilemma affect multitasking performance in flight environments? And which control mode is strategically beneficial in which flight scenarios? To answer these questions, the cognitive control mode of 34 subjects was experimentally manipulated in a multitasking flight environment. A gamification method shifted the participants control mode in a more stable and more flexible control mode respectively. Results show not only differences in the performance of the individual flight tasks, but also in the subjective workload and various eye tracking metrics. The latter could be taken into account by a cognitive assistance system to detect the control mode of pilots in real time. It enables appropriate assistance to be provided, taking into account the control mode and situational demands. Ultimately, this leads to the provision of situation-specific assistance with the potential to enhance the overall safety in the cockpit.
Affect-adaptive tutoring systems detect the current emotional state of the learner and are capable of adequately responding by adapting the learning experience. Adaptations could be employed to manipulate the emotional state in a direction favorable to the learning process; for example, contextual help can be offered to mitigate frustration, or lesson plans can be accelerated to avoid boredom. Safety-critical situations, in which wrong decisions and behaviors can have fatal consequences, may particularly benefit from affect-adaptive tutoring systems, because accounting for affecting responses during training may help develop coping strategies and improve resilience. Effective adaptation, however, can only be accomplished when knowing which emotions benefit high learning performance in such systems. The results of preliminary studies indicate interindividual differences in the relationship between emotion and performance that require consideration by an affect-adaptive system. To that end, this article introduces the concept of Affective Response Categories (ARCs) that can be used to categorize learners based on their emotion-performance relationship. In an experimental study, N = 50 subjects (33% female, 19–57 years, M = 32.75, SD = 9.8) performed a simulated airspace surveillance task. Emotional valence was detected using facial expression analysis, and pupil diameters were used to indicate emotional arousal. A cluster analysis was performed to group subjects into ARCs based on their individual correlations of valence and performance as well as arousal and performance. Three different clusters were identified, one of which showed no correlations between emotion and performance. The performance of subjects in the other two clusters benefitted from negative arousal and differed only in the valence-performance correlation, which was positive or negative. Based on the identified clusters, the initial ARC model was revised. We then discuss the resulting model, outline future research, and derive implications for the larger context of the field of adaptive tutoring systems. Furthermore, potential benefits of the proposed concept are discussed and ethical issues are identified and addressed.
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