The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and...

Area of Science:

• Cognitive psychology
• Human factors in aviation
• Complex systems analysis

Background:

• Understanding air traffic controller (ATCo) conflict resolution is crucial for aviation safety and efficiency.
• Linear models are inadequate for explaining the complex cognitive processes involved.
• A novel network disentangling model is proposed, viewing ATCo behavior as breaking down a relational complexity network.

Purpose of the Study:

• To validate a network disentangling model for explaining ATCo conflict resolution performance.
• To investigate the role of the network rigidity index (NRI) in task demand and operational outcomes.
• To assess the predictive power of the model through cross-validation.

Main Methods:

• Twenty-one professional ATCos participated in 27 conflict resolution scenarios with varying NRI.
• Multilevel regression analyses examined the NRI's impact on interventions, workload, and unresolved rates.
• Cross-validation was employed to ensure model robustness and predictive accuracy.

Main Results:

• The network rigidity index (NRI) demonstrated a curvilinear relationship with the number of ATCo interventions, influencing mental workload.
• The discrepancy between interventions and NRI strongly correlated with the unresolved rate.
• Cross-validation confirmed the robustness and reliability of the model's predictions.

Conclusions:

• The network disentangling model offers a theory-driven framework for understanding ATCo conflict resolution workload and performance.
• The model effectively explains intervention probability and other critical outcomes.
• This approach has potential applications in workload management, sector design, and decision support tools.