Dummy Model Based Workload Modeling

In this paper, we show how a model of human cognition based on ACT-R can be improved to accurately predict cognitive performance under different workload levels. For this purpose, we propose a novel approach which uses an EEG-based workload model to (de-)activate a dummy model which runs in parallel to the actual task model. The dummy model consumes cognitive resources to reflect the effect of workload on behavior and performance. We evaluate the approach in two user studies with different tasks and show a significant reduction of prediction error.