Impact of Automation and Task Load on Unmanned System Operator's Eye Movement Patterns

  • Authors:

  • Cali M. Fidopiastis;Julie Drexler;Daniel Barber;Keryl Cosenzo;Michael Barnes;Jessie Y. Chen;Denise Nicholson

  • Affiliations:

  • Applied Cogntion and Training in Immersive Virtual Enviroments (ACTIVE) Laboratory Institute of Simulation and Training (IST), University of Central Florida,;Applied Cogntion and Training in Immersive Virtual Enviroments (ACTIVE) Laboratory Institute of Simulation and Training (IST), University of Central Florida,;Applied Cogntion and Training in Immersive Virtual Enviroments (ACTIVE) Laboratory Institute of Simulation and Training (IST), University of Central Florida,;U.S. Army Research Laboratory (ARL) - Human Research & Engineering Directorate,;U.S. Army Research Laboratory (ARL) - Human Research & Engineering Directorate,;U.S. Army Research Laboratory (ARL) - Human Research & Engineering Directorate,;Applied Cogntion and Training in Immersive Virtual Enviroments (ACTIVE) Laboratory Institute of Simulation and Training (IST), University of Central Florida,

  • Venue:
  • FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
  • Year:
  • 2009

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Abstract

Eye tracking under naturalistic viewing conditions may provide a means to assess operator workload in an unobtrusive manner. Specifically, we explore the use of a nearest neighbor index of workload calculated using eye fixation patterns obtained from operators navigating an unmanned ground vehicle under different task loads and levels of automation. Results showed that fixation patterns map to the operator's experimental condition suggesting that systematic eye movements may characterize each task. Further, different methods of calculating the workload index are highly correlated, r(46) = .94, p = .01. While the eye movement workload index matches operator reports of workload based on the NASA TLX, the metric fails on some instances. Interestingly, these departure points may relate to the operator's perceived attentional control score. We discuss these results in relation to automation triggers for unmanned systems.