Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control
Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control
复制标题
将三个现有模型扩展到自动化信任分析：信号检测、统计参数估计和基于模型的控制
DOI：
10.1177/0018720819829951
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
T. Sheridan
中科院分区：
文献类型：
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作者：
T. Sheridan
目的：提出自动化中信任的三种量化模型。背景：当前的自动化信任文献包括各种定义和框架，本文对这些定义和框架进行了综述。方法：这项研究展示了如何修改和重新解释现有的三个模型，即信号检测、统计参数估计校准和基于内部模型的控制模型，以应用于对人机交互设计有用的自动化信任。结果：给出了定量和图形的重新解释，讨论了信任和信任校准的度量方法，并给出了应用实例。结论：所建立的模型可用于在未来的实验或系统设计中提供定量的信任度量。应用：提供简单的例子来解释模型应用如何在与信号检测、参数估计校准和基于模型的开环控制相对应的三个信任上下文中工作。
Objective: The objective is to propose three quantitative models of trust in automation. Background: Current trust-in-automation literature includes various definitions and frameworks, which are reviewed. Method: This research shows how three existing models, namely those for signal detection, statistical parameter estimation calibration, and internal model-based control, can be revised and reinterpreted to apply to trust in automation useful for human–system interaction design. Results: The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application. Conclusion: The resulting models can be applied to provide quantitative trust measures in future experiments or system designs. Applications: Simple examples are provided to explain how model application works for the three trust contexts that correspond to signal detection, parameter estimation calibration, and model-based open-loop control.