Abstract
Operators on naval ships have to act in dynamic, critical and high-demand 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.
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Neerincx, M.A., Kennedie, S., Grootjen, M., Grootjen, F. (2009). Modeling the Cognitive Task Load and Performance of Naval Operators. In: Schmorrow, D.D., Estabrooke, I.V., Grootjen, M. (eds) Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience. FAC 2009. Lecture Notes in Computer Science(), vol 5638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02812-0_31
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DOI: https://doi.org/10.1007/978-3-642-02812-0_31
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