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自动化错误偏见,信任和依赖行为在模拟无人机碰撞避免任务中
Austin Jackson1, Tetsuya Sato2, Jeffrey Glassman1
1Old Dominion University, USA.
Human factors
|February 14, 2026
概括
这项研究表明,自动化错误类型,如错误和错误报警,系统地影响用户对无人机避免碰撞系统的信任和依赖. 了解这些偏见是设计可靠的自动化系统的关键.
科学领域:
- 人与计算机的交互
- 认知心理学 认知心理学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 之前的研究强调了自动化错误 (错误,错误报警) 对用户合规性和依赖性的不对称影响.
- 这些不对称效应的根本原因 - - 感知突出与信任校准 - - 仍然不清楚.
研究的目的:
- 调查自动化决策辅助器中的错误偏差如何影响信任和依赖行为.
- 检查错误倾向与虚假警报倾向自动化对用户信任和依赖模拟无人机避免碰撞任务的影响.
主要方法:
- 68名参与者参与了一个模拟无人机监控任务,使用自动化避免碰撞辅助器.
- 帮助的错误偏差被操纵 (错误倾向与错误警报倾向) 具有匹配的感知突出.
- 这些措施包括自动化信任评级和依赖行为 (合规性,依赖性).
主要成果:
- 参与者在与不可靠的自动化交互时,表现出多个因素的信任度下降,无论错误偏差类型如何.
- 与易发生错误的自动化相比,易发生虚假报警的自动化,合规率更高.
- 依赖率显示了相反的模式,在易发生错误报警的自动化中更高.
结论:
- 自动化错误偏差系统地影响用户的合规性和依赖性行为.
- 突出匹配的错误和错误报警错误降低了用户的信任,可能会阻碍基于绩效的信任发展.
- 系统设计人员必须考虑错误类型对依赖性的影响,以适当校准信任并提高透明度.
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