A quantum-inspired model for human-automation trust in air traffic controllers derived from functional Magnetic Resonance Imaging and correlated with behavioural indicators

Abstract

Steady growth in air traffic has resulted in a greater prevalence in automation aids as far as the field of Air Traffic Management is concerned. This has ensued in human factors, particularly trust becoming an essential point of consideration in Air Traffic Controller (ATCO)-automation teams. An undertaking to better embody trust behaviours in ATCOs was attempted by coalescing two schools of thought on trust using the principles of superposition and complementarity from quantum mechanics. This model was further refined with behavioural indicators from the experiment. Brain imaging verification of this synchronised coexistence of both philosophies was established with the use of functional Magnetic Resonance Imaging (fMRI) data, where ATCOs were given conflict detection tasks with the aid of ATS-CAP software that was able to generate credible flight plans with visible waypoints and airports. Data from self-reported questionnaires have been useful in building generalised models of trust. However, the robustness of the model that has been proposed in this paper is higher than generalised models because of the utilisation of unbiased data to represent specifically ATCO trusting behaviour under uncertainty. This is an improvement on current models that are also context-dependent and based on subjective data.