通过棋类任务研究人机协作中控制权的动态切换机制。
Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling
- 设计手脑棋局实验，观察用户在控制权高低间切换行为。
- 发现切换前注视模式与任务难度有显著差异，且后续走法质量提升。
- 构建轻量模型预测切换，准确率达F1=0.65，适合实时系统集成。
人机协作通常采用两种控制层级：引导模式（AI提建议，人决策）与委托模式（AI自主行动，人在约束下控制）。在机器人手术或驾驶辅助等场景中，系统常忽略任务过程中用户偏好随信任、决策复杂度和控制感变化而发生的动态转变。本文通过手脑棋实验，研究用户在序列决策任务中如何动态切换控制层级。参与者在“脑模式”（选子由AI决定走法）与“手模式”（由AI选子，人决定走法）间交替。收集了8名参与者超过400次控制权切换决策，以及注视轨迹、情绪状态和子任务难度数据。统计分析显示，切换前注视模式与子任务复杂度存在显著差异，且后续走法质量更高。基于此，我们提取行为与任务特征，训练出一个轻量级预测模型，实现F1=0.65的切换预测性能。结果表明，实时行为信号可作为系统驱动切换机制的补充输入。定性分析进一步揭示感知AI能力、决策复杂度与控制感是影响切换的关键因素。这些发现有助于设计能根据用户意图与任务需求动态调整控制层级的共治系统。
原文摘要 · Abstract (English)
Human-AI collaboration is typically offered in one of two of user control levels: guidance, where the AI provides suggestions and the human makes the final decision, and delegation, where the AI acts autonomously within user-defined constraints. Systems that integrate both modes, common in robotic surgery or driving assistance, often overlook shifts in user preferences within a task in response to factors like evolving trust, decision complexity, and perceived control. In this work, we investigate how users dynamically switch between higher and lower levels of control during a sequential decision-making task. Using a hand-and-brain chess setup, participants either selected a piece and the AI decided how it moved (brain mode), or the AI selected a piece and the participant decided how it moved (hand mode). We collected over 400 mode-switching decisions from eight participants, along with gaze, emotional state, and subtask difficulty data. Statistical analysis revealed significant differences in gaze patterns and subtask complexity prior to a switch and in the quality of the subsequent move. Based on these results, we engineered behavioral and task-specific features to train a lightweight model that predicted control level switches ($F1 = 0.65$). The model performance suggests that real-time behavioral signals can serve as a complementary input alongside system-driven mode-switching mechanisms currently used. We complement our quantitative results with qualitative factors that influence switching including perceived AI ability, decision complexity, and level of control, identified from post-game interview analysis. The combined behavioral and modeling insights can help inform the design of shared autonomy systems that need dynamic, subtask-level control switches aligned with user intent and evolving task demands.
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