Lastly, regarding cognitive processes, the interpretation
of the gaze data itself has to be considered.
Current approaches using cognitive modeling
and machine learning to predict and classify gaze
behavior (for example, detecting arousal or vigilance)
need further development to provide more
information than just distributions of attention.
In our example, this information could be applied
to weight the AOI circles. Additional information
from measured pupil dilation can be included because
current eye-tracking devices already record
this data and preliminary work to correlate pupil
changes with emotional states already exists.
Supplementary sensors (such as heart rate sensors)
can also provide such information and are already
combined with mobile eye tracking.