通过对话识别团队认知偏差，预测协作失误。
Are you with me? A Framework for Detecting Mental Model Discrepancies in Task-Based Team Dialogues
- 从对话中自动识别四类认知差异：错误信念、无依据信念等。
- 历史偏差次数能有效预测未来认知错位，平均准确率可观。
- 适合研究人机协作或团队沟通的学者使用。
人类常通过自然语言向队友同步任务进展。由于并非所有更新都被传达，团队成员间会产生认知差异，进而影响整体协作表现。如何分类这些差异？对话中检测到的认知错位能否预示未来的不一致？传统共享心智模型（SMM）评估依赖事后专家标注，难以捕捉实时协作动态。本文提出一种框架，可自动识别并分类四类认知差异：无依据信念、错误信念、信念矛盾和遗漏。基于20组双人团队在四个递进层级的协作物体识别任务中的对话数据，我们验证了这些差异模式具有预测能力。以历史偏差次数为特征，采用均匀加权的基线方法即可实现有意义的预测准确率，且不同类型的偏差预测能力存在差异。
原文摘要 · Abstract (English)
Humans typically use natural language to update teammates on task states. Since not all updates are communicated, discrepancies arise between the team members' mental models that negatively affect overall team performance. How can we categorize such discrepancies? Do misalignments detected in team dialogue predict future mental model misalignments? Traditional shared mental model (SMM) assessment methods rely on retrospective expert coding that cannot capture real-time coordination dynamics. We propose a framework to identify and categorize four types of mental model discrepancies: unsupported beliefs, false beliefs, belief contradictions, and omissions, all of which can naturally emerge in team dialogues. Using dialogues from twenty dyad teams performing collaborative object identification tasks across four sequential levels, we demonstrate that these discrepancy patterns contain predictive signals. Averaging historical discrepancy counts achieves meaningful prediction accuracy using uniform weighting as an exploratory baseline, with differential predictability across discrepancy types.
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