Changes in 6 measures of eye activity were assessed as a function of task workload in a target identification memory task. 11 subjects completed 4, 2-hour blocks of a mock anti-air-warfare task, in which they were required to examine and remember target classifications (friend or foe) for subsequent prosecution (fire on/allow to pass), while targets moved steadily toward 2 centrally located ship icons. Target density served as the task workload variable; between 1 and 9 targets were simultaneously present on the display. For each participant, moving estimates of blink frequency and duration, fixation frequency and dwell time, saccadic extent, and mean pupil diameter, integrated over periods of 10 to 20 s, demonstrated systematic changes as a function of target density. Nonlinear regression analyses found blink and fixation frequency and pupil diameter to be the most predictive variables relating eye activity to target density. Participant specific artificial neural network models, developed through training on 2-3 sessions and subsequently tested on a different session from the same participant, correlated well with actual target density levels. Results show that moving mean estimation and artificial neural network techniques enable information from multiple eye measures to be combined to produce reliable near real-time indicators of workload in some visuospatial tasks.
Abstract