Automated diagnostic aids can assist human operators in signal detection tasks, providing alarms, warnings, or diagnoses. Operators often use decision aids poorly, though, falling short of best possible performance levels. Previous research has suggested that operators interact with binary signal detection aids using a sluggish contingent cutoff (CC) strategy (Robinson & Sorkin, 1985), shifting their response criterion in the direction stipulated by the aid's diagnosis each trial but making adjustments that are smaller than optimal. The present study tested this model by examining the efficiency of automation-aided signal detection under different levels of task difficulty. In a pair of experiments, participants performed a numeric decision-making task requiring them to make signal or noise judgments on the basis of probabilistic readings. The mean reading values of signal and noise states differed between groups of participants, producing two levels of task difficulty. Data were fit with the CC model and two alternative accounts of automation-aided strategy: a discrete deference (DD) model, which assumed participants defer to the aid on a subset of trials and a mixture model, which assumed that participants choose randomly between the CC and DD strategies every trial. Model fits favored the mixture model. The results indicate multiple forms of inefficiency in operators' strategies for using signal detection aids. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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识别自动化辅助信号检测中的低效策略。

自动诊断辅助设备可以协助操作员执行信号检测任务，提供警报、警告或诊断。然而，操作员常常未能很好地使用决策辅助工具，未能达到最佳性能水平。先前的研究表明，操作员使用缓慢的偶然截止（CC）策略与二进制信号检测辅助设备进行交互（Robinson＆Sorkin，1985），将其响应标准向每次试验中辅助设备诊断规定的方向移动，但进行的调整幅度小于最佳的。本研究通过检查不同任务难度级别下自动化辅助信号检测的效率来测试该模型。在两项实验中，参与者执行了一项数字决策任务，要求他们根据概率读数做出信号或噪声判断。各组参与者的信号和噪声状态的平均读数值不同，从而产生两个级别的任务难度。数据符合 CC 模型和自动化辅助策略的两个替代说明：离散服从 (DD) 模型，假设参与者服从试验子集的帮助；混合模型，假设参与者在CC 和 DD 策略每次试验。模型拟合有利于混合模型。结果表明，操作员使用信号检测辅助设备的策略存在多种形式的低效率。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。