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.

Previous research has suggested that information and action automation stages do not imply the same consequences for human performance in the supervision of automated systems. Still, only a few studies have simultaneously investigated these stages. When information and action automation are reliable, both can support performance. However, with unreliable aids, the literature has suggested that action automation tends to be more detrimental than information automation. This study aimed to assess the contributions of imperfect information and action automation on attentional allocation and to investigate a potential monitoring inefficiency in a multitasking environment. Participants (n = 96) completed three Multi-Attribute Task Battery (MATB) tasks. A monitoring task was automated with two types of automation (action or information) of four reliabilities each (0%; 56.25%; 87.5%; 100%). Ocular behaviors and performance were assessed. Results show that reliability of information automation influenced visual resource allocation. When information automation was the most reliable, participants spent the least amount of time sampling the monitoring task. Finally, the reliability of action automation triggered no effect on performance or cumulative dwell times. Our results suggest that in complex multitasking situations where information and action automation occurred simultaneously, participants allocated fewer visual resources to automated task with increased information automation reliability. Similarly, their performance was better only with increased information automation.