Computational Cognitive Modeling of Pilot Performance in Pre-Flight and Take-Off Procedures

This study presents a computational cognitive model of pilot performance during pre-flight and take-off procedures. Using the Queueing Network-Adaptive Control of Thought-Rational (QN-ACTR) cognitive architecture, we developed a pilot model capable of simulating the complex cognitive and motor processes involved in flight preparation and departure. The model was connected to the X-Plane flight simulator to generate realistic performance data including task completion times, mental workload estimates, and situation awareness metrics. Human pilot data from controlled experiments were used to validate model predictions and calibrate parameter values. Results demonstrate that the cognitive architecture-based approach can produce flight operation behaviors comparable to human pilots, offering a theory-driven predictive methodology for aviation research and training applications.