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Attention Control and Performance Across Varying Levels of Imperfect Automation

Introducing high-level automation can improve performance and reduce safety concerns. However, an irony of automation is that as systems assume a greater more complex role, the operator may become out of the loop (Onnasch et al., 2014). Automation failures can thus result in catastrophic outcomes. Research has shown that individuals’ ability to notice and recover from these failures may be related to differences in cognition, such as working memory (de Visser et al., 2010; Parasuraman & Jiang, 2012; Saqer et al., 2011). However, investigations into the relationship between working memory and performance with automation have demonstrated mixed results. Some have observed a positive relationship (Rovira et al., 2017) while others have not (Pak et al., 2017). Early research has indicated that attention control (AC) may be a stronger predictor of performance in real-world cognitive tasks (Draheim et al., 2022) as well as with automation (Foroughi et al., 2019) than working memory. The purpose of the current study is to investigate the role of AC when using varying levels of imperfect automation. Previous AC measures have been criticized for their reliance on reaction times and difference scores (Draheim et al., 2021). However, this study employs new measures which are based on performance across 90 seconds. The task utilized information analysis (IA) and medium-decision (MD) forms of automation which was 80% reliable. History-based trust (Lee & Moray, 1994) and trust in automation (Jian et al., 2000) were also collected. Our results demonstrated a positive correlation between attentional control and performance in IA conditions when the automation provided accurate and inaccurate advice. This finding supports previous work done by Foroughi and colleagues. However, these results were not found in MD conditions. This appears to demonstrate a catching up effect where high-level automation reduces performance differences between those with lower and higher AC. Results also demonstrated a positive correlation between self-confidence in completing the task and performance demonstrating that participants were somewhat able to gauge their performance. However, there was not a significant relationship between perceived improvement from the automation and performance even though