  This work proposes a novel approach to include a model of making decision in
human brain into the control loop. Employing the methodology developed in
mathematical neuroscience, we construct a model that accounts for quality of
human decision in supervisory tasks. We specifically focus on adaptive gain
theory and the strategy selection problem. The proposed model is shown to be
capable of explaining the change of a strategy from compensatory to heuristics
in different conditions. We also propose a method to incorporate the effect of
internal and external parameters such as stress level and emergencies in the
decision model. The model is employed in a supervisory controller that
dispatches the jobs between autonomy and a human supervisor in an efficient
way.
