Abstract
Mental workload analysis is an important component in the test and evaluation of humanmachine systems. Existing empirical workload measures have limited applicability when humanin-the-loop tests are impractical, which produces the need for theory-based workload modeling and prediction methods. ACTR-QN is a theory-based integrated cognitive architecture combining the advantages of Adaptive Control of Thought-Rational (ACT-R) and Queueing Network (QN) architectures. The research reported in this paper proposes and examines a theory and method for modeling and visualizing mental workload in ACTR-QN. Validation with an empirical study of a semantic judgment task showed that an ACTR-QN model produced both performance and mental workload data similar to the human results. In addition, different components of the multidimensional mental workload can be visualized with ACTR-QN. Mental workload modeling in ACTR-QN provides a new tool for human factors evaluation of mental workload.
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