ABSTRACT
This chapter provides an alternative approach by developing a computational model of the cognitive processes underlying pilot performance while flying a descent in an automated cockpit. The computational model was built from a cognitive task analysis coupled with empirical performance data. Observations of the problems encountered by the model in flying the simulator suggested a number of interventions that might mitigate error in the cockpit. Two of these interventions were selected for empirical testing. First, model runs and eye track data both suggested that the pilots/model were often unaware of changes in automation mode that were driven by the software rather than the pilot. Second, when the model was interrupted, it often was unable to remember the goal that it was trying to achieve; thus, the model was unable to continue flying.
