Modeling the Cognitive Task Load and Performance of Naval Operators

Abstract. Operators on naval ships have to act in dynamic, critical and highdemand task environments. For these environments, a cognitive task load (CTL) model has been proposed as foundation of three operator support functions: adaptive task allocation, cognitive aids and resource feedback. This paper presents the construction of such a model as a Bayesian network with probability relationships between CTL and performance. The network is trained and tested with two datasets: operator performance with an adaptive user interface in a lab-setting and operator performance on a high-tech sailing ship. The “Naïve Bayesian network ” tuned out to be the best choice, providing performance estimations with 86 % and 74 % accuracy for respectively the lab and ship data. Overall, the resulting model nicely generalizes over the two datasets. It will be used to estimate operator performance under momentary CTL-conditions, and to set the thresholds of the load-mitigation strategies for the three support functions

Exploring Individual Differences in Workload Assessment

Air Force missions continue to increase in complexity often imposing higher levels of task load from cognitive tasks on the operators. This increased task load manifests itself in increased cognitive workload and potentially derogated performance. While cognitive workload has been studied for decades, recent advances in objective workload models and physiology monitoring have the potential to provide a more robust understanding of workload, potentially allowing systems to adaptively employ automation to maintain operator peak performance. The current research sought to provide insight into the relationship between subjective workload, task performance, objective workload, and select physiology measures. Analysis of an existing data set was performed to determine if individuals exhibiting low performance and high workload were more likely to have physiology responses that increased with workload due to a stress response than other participants. This analysis provides an approach to investigating the relationships among the four classes of workload information. However, the results indicate that certain physiology measures are significantly correlated with objective workload, regardless of the performance and workload range of the participants. Unfortunately, relatively low correlations were observed among all dependent measures and therefore, further research is necessary to confidently address the hypothesis of the current research