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Cue utilisation predicts control room operators' performance in a sustained visual search task.

Daniel SturmanMark W WigginsJaime C AutonWilliam S Helton
Published in: Ergonomics (2019)
This research was designed to determine whether qualified practitioners' cue utilisation is predictive of their performance during a sustained visual search task in an operational context. Australian Distribution Network Service Provider (DNSP) operators were recruited for two experiments, and were classified with either higher or lower cue utilisation based on an assessment of cue utilisation within the context of power distribution. Operators' performance was assessed using a domain-related sustained visual search task. In both experiments, power distribution operators with higher cue utilisation demonstrated shorter mean response latencies during the sustained visual search task, compared to operators with lower cue utilisation. Further, no differences in accuracy based on cue utilisation were observed during the sustained visual search task. The results are consistent with the proposition that power operators with higher cue utilisation have a greater capacity to sustain visual search during domain-related tasks, compared to operators with lower cue utilisation. Practitioner summary: Power distribution system operators' cue utilisation was used to predict performance during a domain-related sustained visual search task. Power distribution operators with higher cue utilisation demonstrated shorter mean response latencies during the sustained visual search task, but no differences in accuracy, compared to operators with lower cue utilisation. Abbreviations: DNSP: distribution network service provider; EXPERTise 2.0: EXPERT intensive skills evaluation; FAT: feature association task; FDT: feature discrimination task; FIT: feature identification task; fNIRS: functional near infrared spectroscopy; FPT: feature prioritisation task; FRT: feature recognition task; SCADA: supervisory control and data acquisition.
Keyphrases
  • machine learning
  • primary care
  • deep learning
  • healthcare
  • mental health
  • big data
  • electronic health record