Individual ability-based system configuration cognitive profiling with Bayesian Networks

In this paper, we present a new method to model individual cognitive abilities, which were originally described by structural equation models and which can be used to reduce the possibility of human error when interacting with complex technical system. Our proposed model is based on hierarchical Bayesian networks and deals therefore with uncertain and noisy data. The validity of the method is demonstrated on the basis of simulations of real-life data. The inference from the Bayesian network demonstrates that estimating the user's cognitive abilities is possible in a reliable manner in real-time. Structural and data dependency analyses further show that less evidence data are necessary to provide cognitive profile classification. This especially highlights the advantage of our new method in comparison to the - in psychology - traditionally used structural equation models. Such cognitive profiling will allow adapting a system's demand character to the cognitive ability level of an individual user, which will make operating that system less error-prone, as many human errors result out of systems requesting more cognitive abilities than the operator has available.