Mental Workload Modeling in an Integrated Cognitive Architecture

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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