Human-machine automation systems involve integration among automated agents and goal-oriented human operators. Automation fundamentally changes the nature of the cognitive demands placed on the human operator, while simultaneously increasing the potential for out-of-the-loop performance problems to impact system efficacy. As a novel human factors method for assessing these human-automation interaction-derived, out-of-the-loop, performance challenges, we introduce the Human-Automation Behavioral Interaction Task (HABIT) analysis. The HABIT framework considers the drivers of system performance in terms of both cognitive activity and human behavior. As an example, we apply HABIT to a tedious intelligence, surveillance, and reconnaissance task. HABIT demonstrates its effectiveness in guiding the modeling and annotation of cognitive activities and successfully predicting actions resulting in errors. System designers can apply HABIT to assess human-automation interaction before a system is fielded to proactively address and mitigate out-of-the-loop performance problems.

中文翻译：

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人机交互任务（HABIT）分析框架

人机自动化系统涉及自动化代理和面向目标的人类操作员之间的集成。自动化从根本上改变了对人类操作员的认知需求的性质，同时增加了影响系统效能的潜在性能问题。作为一种用于评估这些人机交互衍生的、外循环的性能挑战的新的人为因素方法，我们引入了人机交互行为任务 (HABIT) 分析。HABIT 框架从认知活动和人类行为两个方面考虑系统性能的驱动因素。例如，我们将 HABIT 应用于繁琐的情报、监视和侦察任务。HABIT 证明了它在指导认知活动的建模和注释以及成功预测导致错误的行为方面的有效性。系统设计人员可以在系统投入使用之前应用 HABIT 来评估人机交互，从而主动解决和缓解环路外性能问题。