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.

中文翻译：

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在涉及风险的高工作负载多任务处理场景下，自动化信任增加

信任是一个关键结构，它影响涉及不完善自动化的多任务工作空间中的人机交互。卡尔平斯基等人。(Appl Ergon, 70, 194–201, 2018) 使用多属性任务电池 II (MATB) 研究了信任是否会影响操作员在高负载场景中的注意力分配。结果表明，任务负载降低了对不完美自动化的信任，然后减少了对自动化辅助的监控任务的视觉注意力分配。参与者还报告了在高负载条件下的信任度降低。然而，高负载条件下的参与者可能不信任系统，因为他们糟糕的任务表现没有明确的不利后果（即风险）。当前的实验旨在复制和扩展 Karpinsky 等人。(2018) 通过要求 40 名参与者在有或没有风险的情况下在 MATB II 中同时执行跟踪任务和系统监控任务。支持系统监控任务的自动化辅助设备的可靠性为 70%。该研究采用 2 × 2 裂区设计，其中任务负荷（简单与困难）通过跟踪任务中的错误量级作为参与者内因素和风险（高与低）作为参与者间因素。高风险组的参与者收到一条指示，即表现不佳会导致重复实验，而低风险组的参与者则没有收到这条指示。结果表明，高负载和低负载条件之间的信任是可比的，但高风险会提高高负载条件下的信任。