Decision-making efficiency with aided information: the impact of automation reliability and task difficulty - Hanshu Zhang; Ran Zhou; Cheng-You Cheng; Sheng-Hsu Huang; Ming-Hui Cheng; Cheng-Ta Yang (Cognitive Research)
Abstract Although it is commonly believed that automation aids human decision-making, conflicting evidence raises questions about whether individuals would gain greater advantages from automation in difficult tasks. Our study examines the combined influence of task difficulty and automation reliability on aided decision-making. We assessed decision efficiency by employing the single-target self-terminating (STST) capacity coefficient in Systems Factorial Technology, estimating the ratio of performance with aided information to that without it. Participants were instructed to perform a shape categorization task, wherein they assessed whether the presented stimulus belonged to one category or another. In Experiment 1, three automation reliability conditions (high reliability, low reliability, and unaided) were tested in separate blocks. Our results indicated that, in general, participants exhibited unlimited capacity when provided with valid automated cues, implying that the decision efficiency was unaltered by automated assistance. Despite the failure to gain extra efficiency, the benefits of automated aids in decision-making for difficult tasks were evident. In Experiment 2, various types of automation reliability were randomly intermixed. In this scenario, the impact of automation reliability on participants’ performance diminished; however, the significance of information accuracy increased. Our study illustrates how the presentation of automation, its reliability, and task difficulty interactively influence participants’ processing of automated information for decision-making. Our study may improve processing efficiency in automated systems, hence facilitating superior interface design and automation execution.