Various cognitive modeling techniques and tools have been developed to support description and prediction of human behavior in complex systems. GOMS (Goals, Operators, Methods and Selection rules) modeling methods have been used in human–computer interaction (HCI) analysis for many years and are considered easy to learn. GOMS has several limitations, including representing only expert behavior in tasks and not supporting detailed modeling of visual and motor operations or parallel processing. Another limitation is that operation time estimates are deterministic. This research developed an enhanced GOMS language and computational cognitive modeling tool to address the existing GOMS limitations to aid cockpit automation designers in assessing the potential for automation-induced pilot performance problems. Output of the tool for a specific flight and automation use scenario was compared with experiment data for validation purposes. Results demonstrated significant correlations of model-based pilot performance and cognitive workload predictions with observations on pilots using a flight simulator. The new enhanced cognitive modeling approach is expected to provide accurate explanations and predictions of user behaviors during the design of complex systems and interfaces in various domains involving interactive task performance.