| Abstract |
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 subject's 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 poor
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.
|