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Automation Type and Reliability Impact on Visual Automation Monitoring and Human Performance

Abstract : We compared automation monitoring evolution of static or adaptive automation for four different reliability levels over 90 minutes. Previous studies have demonstrated degraded human performance when monitoring automation and that it is possible to mitigate this monitoring performance drop by using adaptive automation. We used the Open Multi-Attribute Task Battery to manipulate two type of automation (static automation without manual take-over sessions and adaptive automation with planned take-over sessions) and four levels of reliability. Participants performed three simultaneous tasks, one of which was automated. Our results suggest that a perfectly reliable or a totally unreliable automation led to different strategies by the participants in terms of visual allocation policy. Under static automation, the time spent looking at the automated task in the 0% reliability level increased over the duration of the experiment; however, the opposite was observed for the 100% reliability level. Although similar, the magnitude of this pattern of results was largely diminished under adaptive automation. For static automation, the reported data also showed a direct link between trust in automation and visual scanning strategies. The more the trust increased, the less the automated task was looked at.
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Contributor : Eugénie Avril Connect in order to contact the contributor
Submitted on : Wednesday, June 30, 2021 - 11:42:25 AM
Last modification on : Wednesday, November 10, 2021 - 6:39:38 PM




Eugénie Avril, Julien Cegarra, Liên Wioland, Jordan Navarro. Automation Type and Reliability Impact on Visual Automation Monitoring and Human Performance. International Journal of Human-Computer Interaction, Taylor & Francis, 2021, pp.1-14. ⟨10.1080/10447318.2021.1925435⟩. ⟨hal-03274590⟩



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