Automation trust increases under high-workload multitasking scenarios involving risk.

Trust is a critical construct that influences human–automation interaction in multitasking workspaces involving imperfect automation. Karpinsky et al. (Appl Ergon, 70, 194–201, 2018) investigated whether trust affects operators' attention allocation in high-load scenarios using the multi-attribute task battery II (MATB). Results suggested that task load reduces trust towards imperfect automation, then reducing visual attention allocation to the monitoring task aided by the automation. Participants also reported reduced levels of trust in high-load conditions. However, it is possible that the participants in high-load conditions did not trust the system because their poor task performance did not have expressly adverse consequences (i.e., risk). The current experiments aimed to replicate and extend Karpinsky et al. (2018) by asking forty participants to concurrently perform a tracking task and system monitoring task in the MATB II with or without risk. The reliability of the automated aid supporting the system monitoring task was 70%. The study employed a 2 × 2 split-plot design with task load (easy vs. difficult) via magnitude of errors in the tracking task as a within-participant factor and risk (high vs. low) as a between-participant factor. Participants in the high-risk group received an instruction that poor performance would result in a repeat of the experiment, whereas participants in the low-risk group did not receive this instruction. Results showed that trust was comparable between the high- and the low-load conditions, but the high risk elevated trust in the high-load condition. This implies that operators display greater levels of trust when a multitasking environment demands greater attention and they perceive risk of receiving expressly adverse consequence, regardless of the true reliability of automated systems. [ABSTRACT FROM AUTHOR]