[2605.03149] Are you with me? A Framework for Detecting Mental Model Discrepancies in Task-Based Team Dialogues
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arXiv:2605.03149 (cs)
[Submitted on 4 May 2026]
Title:Are you with me? A Framework for Detecting Mental Model Discrepancies in Task-Based Team Dialogues
Authors:Katharine Kowalyshyn, Matthias Scheutz
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Abstract: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.
Comments: Accepted to Proceedings of the Annual Meeting of the Cognitive Science Society 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.03149 [cs.AI]
(or arXiv:2605.03149v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.03149
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Katharine Kowalyshyn [view email] [v1] Mon, 4 May 2026 20:42:16 UTC (3,470 KB)
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