Tools for Predicting the Duration and Variability of Skilled

Abstract

Many devices are designed to allow skilled users to complete routine tasks quickly, often within a specified amount of time. Predictive human performance modeling has long been able to predict the mean time to accomplish a task, making it possible to compare device designs before building them. However, estimates of the variability of performance are also important, especially in real-time, safety-critical tasks. Until recently, the human factors community lacked tools to predict the variability of skilled performance. In this paper, we describe a combination of theory-based tools (CogTool and SANLab) that address this critical gap and that can easily be used by human factors practitioners or system designers. We describe these tools, their integration, and provide a concrete example of their use in the context of entering the landing speed into the Boeing 777 Flight Management Computer (FMC) using the Control and Display Unit (CDU).