Training Techniques for Visual Search in Complex Task Environments

Objective:The goal for this study was to evaluate several visual search training techniques in an unmanned aerial vehicle (UAV) simulated task environment.Background:Operators controlling remote unmanned vehicles often must perform complex visual search tasks (e.g., target search). These tasks may pose substantial demands on the operator due to various environmental factors. Visual search training may reduce errors and mitigate stress, but the most effective form of training has not been determined.Methods:Participants were assigned to one of four training conditions: target, cue, visual scanning, or control. After the training, the effectiveness of the training techniques was tested during a 30-minute simulated UAV flight. A secondary task manipulation was included to further simulate the demands of a realistic UAV control and target search task. Subjective stress and fatigue were also assessed.Results:Target training produced superior target search performances in more hits and fewer false alarms (FAs) when compared to the control condition. The visual scanning and cue trainings were moderately effective. Only target training performance was vulnerable to the secondary task load. The task was stressful, but training did not mitigate stress response.Conclusion:Training participants on the target and the cue appearance as well as active scanning of the visual field is promising for promoting effective target search for this simulated UAV environment.Application:These training techniques could be used in preparation for intelligence, surveillance, and reconnaissance (ISR) missions that involve target search, especially where target appearance change is likely.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: pp 1139-1152
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01679217
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 15 2018 9:47AM