Objective: For selection practitioners, it seems important to assess the level of mental resources invested in order to perform a demanding task. In this study, we investigated the potential of pupil size measurement to discriminate the most proficient pilot students from the less proficient. Background: Cognitive workload is known to influence learning outcome. More specifically, cognitive difficulties observed during pilot training are often related to a lack of efficient mental workload management. Method: Twenty pilot students performed a laboratory multitasking scenario, composed of several stages with increasing workload, while their pupil size was recorded. Two levels of pilot students were compared according to the outcome after 2 years of training: high success and medium success. Results: Our findings suggested that task-evoked pupil size measurements could be a promising predictor of flight training difficulties during the 2-year training. Indeed, high-level pilot students showed greater pupil size changes from low-load to high-load stages of the multitasking scenario than medium-level pilot students. Moreover, average pupil diameters at the low-load stage were smallest for the high-level pilot students. Conclusion: Following the neural efficiency hypothesis framework, the most proficient pilot students supposedly used their mental resources more efficiently than the least proficient while performing the multitasking scenario. Application: These findings might introduce a new way of managing selection processes complemented with ocular measurements. More specifically, pupil size measurement could enable identification of applicants with greater chances of success during pilot training.

Poor cockpit monitoring has been identified as an important contributor to aviation accidents. Improving pilots’ monitoring strategies could therefore help to enhance flight safety. During two different sessions, we analyzed the flight performance and eye movements of professional airline pilots in a full-flight simulator. In a pre-training session, 20 pilots performed a manual approach scenario as pilot flying (PFs) and were classified into three groups according to their flight performance: unstabilized, standard, and most accurate. The unstabilized pilots either under- or over-focused various instruments. Their number of visual scanning patterns was lower than those of pilots who managed to stabilize their approach. The most accurate pilots showed a higher perceptual efficiency with shorter fixation times and more fixations on important primary flight instruments. Approximately 10 months later, fourteen pilots returned for a post-training session. They received a short training program and performed a similar manual approach as during the pre-training session. Seven of them, the experimental group, received individual feedback on their own performance and visual behavior (i.e., during the pre-training session) and a variety of data obtained from the most accurate pilots, including an eye-tracking video showing efficient visual scanning strategies from one of the most accurate pilots. The other seven, the control group, received general guidelines on cockpit monitoring. During the post-training session, the experimental group had better flight performance (compared to the control group), and its visual scanning strategies became more similar to those of the most accurate pilots. In summary, our results suggest that cockpit monitoring underlies manual flight performance and that it can be improved using a training program based mainly on exposure to eye movement examples from highly accurate pilots.

For pilots, the capacity to cope with anxiety is crucial during a flight since they may be confronted with stressful situations. According to the Big Five Inventory, this capacity can be modulated by two important personality traits: conscientiousness and neuroticism. The former would be related to concentration skills and the latter to the attention bias towards anxiety-provoking stimuli. Given the current development of monitoring systems for detecting the users’ state, which can be incorporated into cockpits, it is desirable to estimate their robustness to inter-individual personality differences. Indeed, several emotion recognition methods are based on physiological responses that can be modulated by specific personality profiles. The personality traits of twenty pilots were assessed. Afterwards, they performed two consecutive simulated flights without and with induced social stress while electrodermal activity was measured. Their subjective anxiety was assessed before the second flight, prior to the stress-induced condition. The results showed that higher scores in neuroticism correlated positively with cognitive and somatic anxiety. Moreover, under social stress, higher scores in conscientiousness correlated positively with electrodermal stability, i.e., a lower number of skin conductance responses. These results on both self-reported and physiological responses are in favor of the integration of personality differences into pilots’ state monitoring.

Objective: The objective was to characterize multitask resource reallocation strategies when managing subtasks with various assigned values. Background: When solving a resource conflict in multitasking, Salvucci and Taatgen predict a globally rational strategy will be followed that favors the most urgent subtask and optimizes global performance. However, Katidioti and Taatgen identified a locally rational strategy that optimizes only a subcomponent of the whole task, leading to detrimental consequences on global performance. Moreover, the question remains open whether expertise would have an impact on the choice of the strategy. Method: We adopted a multitask environment used for pilot selection with a change in emphasis on two out of four subtasks while all subtasks had to be maintained over a minimum performance. A laboratory eye-tracking study contrasted 20 recently selected pilot students considered as experienced with this task and 15 university students considered as novices. Results: When two subtasks were emphasized, novices focused their resources particularly on one high-value subtask and failed to prevent both low-value subtasks falling below minimum performance. On the contrary, experienced people delayed the processing of one low-value subtask but managed to optimize global performance. Conclusion: In a multitasking environment where some subtasks are emphasized, novices follow a locally rational strategy whereas experienced participants follow a globally rational strategy. Application: During complex training, trainees are only able to adjust their resource allocation strategy to subtask emphasis changes once they are familiar with the multitasking environment.