MODEL-BASED ASSESSMENT OF ADAPTIVE AUTOMATION’S UNINTENDED CONSEQUENCES

Abstract

Recent technological advances require development of human-centered principles for their inclusion into complex systems. While such programs incorporate revolutionary hardware and software advances, there is a necessary space for including human operator design considerations, such as cognitive workload. As technologies mature, it is essential to understand the impacts that these emerging systems will have on cognitive workload. Adaptive automation is a solution that seeks to manage cognitive workload at optimal levels. Human performance modeling shows potential for modeling the effects of adaptive automation on cognitive workload. However, the introduction of adaptive automation into a system can also present unintended negative consequences to an operator. This dissertation investigated potential negative unintended consequences of adaptive automation through the development of human performance models of a multi-tasking simulation. One hundred twenty participants were enrolled in three human-in-the-loop experimental studies (forty participants each) that collected objective and subjective surrogate measures of cognitive workload to validate the models. Results from this research indicate that there are residual increases in operator workload after transitions in system states between manual and automatic control of a task that need to be included in human performance models and in system design considerations.