Quantitative prediction of mental workload with the ACT-R cognitive architecture

A cognitive architecture, such as ACT-R, generally does not have any built-in functions to predict mental workloads even though it can directly represent an operator's cognitive process and predict the operator's performance at the sub-second scale. In this paper, a methodology to quantitatively predict the mental workload with a cognitive architecture, ACT-R, is proposed. A mathematical representation of the mental workload over the time with respect to the activated time of the ACT-R modules was proposed in this paper. Experiments were performed on memorization tasks, visual-manual tasks, and menu selection tasks. In result, it was found that the predicted values of mental workload achieved by the proposed method were highly correlated with the mean of NASA-TLX subjective ratings from the participants. It was proposed that the way of predicting mental workload in this study could be possibly applied to alternative cognitive architectures that have similar attributes to those of ACT-R. In addition, the method proposed in this study can be applied to quantitatively predict an operator's mental workload over time early on in the design phase of dynamic systems. Relevance to Industry: Predicting the mental workload experienced by system operators can provide system designers with useful information to reduce the possibility of human error and cost of training, improve the safety and performance of systems, and achieve operator satisfaction. The method in this study can predict not only the mental workload but also the performance of system operators with one model of ACT-R so that it can be applied to evaluate the system interfaces early on in the design phase.