Use of eye movements as feedforward training for a synthetic aircraft inspection task

Abstract

Aircraft inspection is a vital element in assuring safety and reliability of the air transportation system. The human inspector performing visual inspection of an aircraft is the backbone of this process and training is an effective strategy for improving their inspection performance. Previous studies have shown offline feedback training to be effective in improving subsequent visual inspection performance. Because experienced inspectors are known to adopt a better inspection strategy than novices, providing visualization of experts' cognitive processes a priori can accelerate novices' adoption of the experts' strategy. Using eye tracking equipment, we record the point of regard of an expert inspector performing an inspection task in a virtual reality simulator. Analysis of their eye movements leads to a visualization of their scanpaths and allows us to display the inspector's visual search (hence cognitive) strategy. We show how providing this type of scanpath-based feedforward training of novices leads to improved accuracy performance in the simulator coupled with an observed speed-accuracy tradeoff. We contend that the tradeoff results from trained novices adopting a slower paced strategy through increased fixation durations, suggesting trained novices learn a more deliberate target search/discrimination strategy that requires more time to execute.

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