智能机器时代的人类警:挑战和前景
1Department of Psychology, and Institute for Simulation and Training, University of Central Florida, Orlando, FL, USA.
Ergonomics
|January 12, 2026
概括
人类在自主系统中的监督需要持续的关注,但警度的下降往往是可以避免的. 改进的设计和测量方法,整合精度和延迟,可以提高人类-AI团队的性能.
科学领域:
- 人与计算机的交互
- 认知心理学 认知心理学
- 人类因素工程 人类因素工程
背景情况:
- 在工作系统中增加自主性需要理解人类在协作努力中的角色.
- 当前的人类监督模型强调持续的注意力,导致人们担心警程度下降.
- 需要确定人类监控能力下降的限制和条件.
研究的目的:
- 确定自主系统中人类警的边界条件.
- 评估目前的警性降低措施,并确定它们的局限性.
- 建议改进的方法来评估和缓解人类-AI合作的性能下降.
主要方法:
- 审查和批评现有的警评估措施.
- 探索影响警程度下降的因素.
- 基于增强的测量,制定改善策略的建议.
主要成果:
- 警觉性下降并不是所有人类-人工智能工作环境中不可避免的结果.
- 具体的设计考虑可以防止或减少性能下降.
- 改进的测量整合响应精度和延迟提供了前进的道路.
结论:
- 自主系统中的人类监控能力可以通过深思熟虑的设计来保持.
- 增强的测量技术对于理解和改善混合团队中的人类表现至关重要.
- 优化人类-人工智能协作需要通过更好的评估和设计,专注于减轻警度的下降.
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