Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency---the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions.

中文翻译：

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基于人类信任的反馈控制：动态改变自动化透明度以优化人机交互

人类对自动化的信任在人类与自动化之间的交互中起着至关重要的作用。虽然缺乏信任会导致人们不再使用自动化，但过度信任会导致人们信任有缺陷的自主系统，这可能会对人类产生负面影响。因此，应该校准人类信任，以根据特定于上下文的性能目标优化人机交互。在本文中，我们提出了一个概率框架来建模和校准人类在与智能决策辅助系统交互期间的信任和工作负载动态。这种校准是通过改变自动化的透明度——提供给人类的信息的数量和效用来实现的。该模型的参数化是使用通过人类受试者实验收集的行为数据进行的，三个反馈控制策略经过实验验证并与非自适应决策辅助系统进行比较。结果表明，当基于建议的控制策略动态更新透明度时，可以优化人工自动化团队的绩效。该框架是广泛设计和实现用于人机交互的实时自适应自动化的第一步。