为何选此而非彼？一种用于理解决策支持对话中思维覆盖的协作反思方法
Why This and Not That? A Collaborative Reflection Approach for Understanding Thought Coverage in Decision Making Support Dialog
AI总结：
该研究针对决策支持对话智能体的自适应策略易误判用户状态的问题，提出以人为中心的协作反思方法，经用户研究发现用户解释可指导更恰当的对话动作。
AI中文摘要：
支持决策反思的对话智能体通常依赖自适应对话策略，将观测到的用户行为映射到探查、深化或重定向等动作。然而同一行为模式可能反映多种不同原因，如刻意优先考虑或自我认知有限。当前策略因仅对可观测模式建模而非用户行为原因，可能对用户状态做出过早假设并采取不恰当的后续动作。为解决这一差距，我们提出一种以人为中心的方法，用于揭示这一隐藏推理步骤。在包含62名用户和232个协作时刻的用户研究中，我们在反思支持智能体通常会重定向对话时暂停它，展示其观测结果并要求用户解释该模式并决定如何继续。我们得出包含9种解释类别的分类法，并表明相似的反思状态可能需要截然不同的后续动作。我们的发现挑战了自适应对话策略仅依赖可观测行为的假设，且表明用户提供的解释可指导更恰当的对话动作。
英文摘要：
Conversational agents that support reflection for decision-making often rely on adaptive dialog policies that map observed user behavior to actions such as probing, deepening, or redirecting. Yet the same pattern can reflect a range of different reasons such as deliberate prioritisation or limited self-access. By modeling the observable pattern rather than the user's reason for it, current policies risk premature assumptions about the user state and inappropriate next actions. To address this gap, we introduce a human-centered method for surfacing this hidden inference step. In a user study with 62 users and 232 collaborative moments, we pause a reflection-support agent when it would normally redirect the conversation, surface its observation, and ask users to interpret the pattern and decide how to proceed. We derive a taxonomy of nine interpretation categories and show that similar reflective states can call for substantially different follow-up actions. Our findings challenge the assumption that adaptive dialog policies can rely on observable behavior alone, and suggest how user-provided interpretations can inform more appropriate conversational actions.
