Computational models of attention can be used as a component of decision support systems. For accurate support, a computational model of attention has to be valid and robust. The effects of task performance and task complexity on the validity of three different computational models of attention were investigated in an experiment. The gaze-based model uses gaze behavior to determine where the subjects attention is, the task-based model uses information about the task and the combined model uses both gaze behavior and task information. While performing a tactical compilation task, participants had to indicate to what set of objects their attention was allocated. The indications of the participants were compared with the estimations of the three models. The results show that overall, the estimation of the combined model was better than that of the other two models. Contrary to what was expected, the performance of the models was not different for good and bad performers and was not different for a simple and complex scenario. The difference in complexity and performance might not have been strong enough. Further research is needed to determine if improvement of the combined model is possible with additional features and if computational models of attention can effectively be used in decision support systems. This can be done using a similar validation methodology as presented in this paper.