Neurophysiological correlates of psychological attitudes of air traffic controllers during their work

The research proposed in this thesis is part of a European project called NINA (Neurometrics Indicators for Air Traffic Management) funded by Sesar Joint Undertaking, and it involves the participation of Sapienza University of Rome, École Nationale de l’Aviation Civile (ENAC), and Deep Blue srl (Human Factor and Safety Consultant Company). The main goal of the project is to elaborate neurophysiological measurements for real-time assessment and monitoring of the cognitive state in particular professional categories, such as Air Traffic Controllers (ATCOs). The evaluation is performed by using a combination of techniques such as Electroencephalography (EEG), Electrocardiography (EKG) and Electrooculography (EOG), during simulated and realistic working conditions. In the area of ATCOs, the Skill, Rule and Knowledge (S-R-K) taxonomy was developed by Rasmussen to describe the human performance under various circumstances and to integrate a variety of research results coming from human cognition studies (attention, memory, problem solving, decision-making, etc.) under a common framework. It provides a description of human cognition that is functional to the understanding and prediction of behaviour: it specifically deals with how people control their activity and behave in interaction with complex systems. Therefore, by considering the aspect of the cognitive processes in the framework of such taxonomy, it is possible to contextualise them in the work practices. Since to our knowledge there are no corresponding studies in the existing literature, another challenging objective of the project is to develop the SRK concept from a neurophysiological point of view. The focus of the proposed thesis is thus to verify the existence of identifiable neurophysiological features associated to the three levels of cognitive control of behaviour (Skill, Rule and Knowledge), in Air Traffic Management (ATM) context, by using a neurometric able to identify the behaviours of the original taxonomy from a different perspective. To map the neurophysiology of the SRK framework in ATM domain, and to use this methodology, could represent a promising step forward into the analysis of human behaviour, and furthermore, to develop new Human Factors tools able to discriminate the level of operators’ expertise during ecological tasks. In detail, the first part of this work illustrates a brief description of the brain and the Electroencephalographic technique, then an introduction of the NINA project and the literature related to the S-R-K levels of cognitive control are presented. The second section is focused on some additional brain features’ literature and on the experimental phase where several steps were performed as follows: a) the three categories of behaviours were associated with specific cognitive functions (e.g. attention, memory, decision making etc.) already investigated in literature with EEG measurements; b) a link between S-R-K behaviours and expected EEG frequency bands configurations were hypothesized; c) specific events were designed to trigger S, R and K behaviours and integrated into realistic ATM simulations; d) finally, the machine-learning algorithm automatic stop StepWise Linear Discriminant Analysis (asSWLDA) was trained to differentiate the three levels of cognitive control of behaviour by using brain features extracted from the EEG rhythms of different brain areas. Several professional ATCOs from the École Nationale de l’Aviation Civile (ENAC) of Toulouse (France) were involved in the study and the results showed that the classification algorithm was able to discriminate with high reliability the three levels of cognitive control of behaviour during simulated air-traffic scenarios in an ecological ATM environment.