A proposed methodology for the theory-based prediction of mental workload with a cognitive architecture

In the early stage of system development, a prototype that is adequate for testing will be unavailable; then, it would be difficult to empirically predict mental workload through various measures. For this reason, task analytic methods based on task analysis probably have been the most popular for predicting mental workload though they are somewhat task-specific. A theory-based cognitive architecture could directly represent an operator's cognitive processes and make predictions for cognitive activity in general situations at the sub-second scale. In this study, a methodology to predict the mental workload with a cognitive architecture is proposed. In empirical results, subjective measures of the mental workload with NASA-TLX were well matched with model predictions. It is suggested that the proposed method could be generally applied to predict mental workload in an early design phase.