This thesis used a maritime classification task to examine the extent to which humans can accurately estimate automation reliability and calibrate to changes in reliability, and factors impacting the acceptance of automated advice. The research outcomes indicate individuals do not initially calibrate to, or accurately calibrate to changes in, automation reliability. Acceptingautomated advice was predicted by greater positive differences between participant assessments of automation reliability and their own manual reliability. The findings have important theoretical and practical implications for human-automation teaming.
|Qualification||Doctor of Philosophy|
|Award date||22 May 2022|
|Publication status||Unpublished - 2022|