  This paper outlines the development and testing of a novel, feedback-enabled
attention allocation aid (AAAD), which uses real-time physiological data to
improve human performance in a realistic sequential visual search task. Indeed,
by optimizing over search duration, the aid improves efficiency, while
preserving decision accuracy, as the operator identifies and classifies targets
within simulated aerial imagery. Specifically, using experimental eye-tracking
data and measurements about target detectability across the human visual field,
we develop functional models of detection accuracy as a function of search
time, number of eye movements, scan path, and image clutter. These models are
then used by the AAAD in conjunction with real time eye position data to make
probabilistic estimations of attained search accuracy and to recommend that the
observer either move on to the next image or continue exploring the present
image. An experimental evaluation in a scenario motivated from human
supervisory control in surveillance missions confirms the benefits of the AAAD.
