Using Computational Cognitive Modeling to Diagnose Possible Sources of Aviation Error

We present a computational model of a closed-loop, pilot–aircraft–visual scene–taxiway system created to shed light on possible sources of taxi error. The creation of the cognitive aspects of the model with ACT–R (Adaptive Control of Thought–Rational) required us to conduct studies with subject matter experts to identify the experiential adaptations pilots bring to taxiing. Five decision strategies were found, ranging from cognitively intensive but precise to fast and frugal but robust. We provide evidence for the model by comparing its behavior to a National Aeronautics and Space Administration Ames Research Center simulation of Chicago O’Hare surface operations. Decision horizons were highly variable; the model selected the most accurate strategy given the time available. We found a signature in the simulation data of the use of globally robust heuristics to cope with short decision horizons as revealed by the errors occurring most frequently at atypical taxiway geometries or clearance routes. These data provided empirical support for the model.