研究人机协作中相互心智理论对团队表现的影响
Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task
- 用大模型驱动的具心智理论能力的AI代理参与实时共享任务
- 双向沟通虽增强人类对AI的理解但降低团队整体表现
- 适合关注人机协作设计与实时交互体验的研究者
心智理论（ToM）是影响人类协作与沟通的关键能力，能理解他人意图。当具备心智理论能力的AI代理与人类协作时，会形成相互心智理论（MToM）过程，该过程涉及交互沟通与基于心智理论的策略调整，进而影响团队表现与协作流程。为探究此过程，我们开展一项混合设计实验，在实时共享工作空间任务中使用具备心智理论与沟通模块的大语言模型驱动的AI代理。结果表明，尽管AI的心智理论能力未显著提升团队性能，但显著增强了人类对AI的理解及被理解的感受。多数参与者认为言语沟通增加负担，且双向沟通导致团队表现下降。研究讨论了上述结果对实时共享工作空间任务中人机协作系统设计的启示。
原文摘要 · Abstract (English)
Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. When AI agents with ToM capability collaborate with humans, Mutual Theory of Mind (MToM) arises in such human-AI teams (HATs). The MToM process, which involves interactive communication and ToM-based strategy adjustment, affects the team's performance and collaboration process. To explore the MToM process, we conducted a mixed-design experiment using a large language model-driven AI agent with ToM and communication modules in a real-time shared-workspace task. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent and the feeling of being understood. Most participants in our study believe verbal communication increases human burden, and the results show that bidirectional communication leads to lower HAT performance. We discuss the results' implications for designing AI agents that collaborate with humans in real-time shared workspace tasks.
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