Estimating Shared Mental Models via Communication-Categorized Directed Graphs

Corporate organizations face increasingly complex tasks that demand effective team management. A key concept is the Shared Mental Model (SMM), which enables members to maintain performance despite limited communication. Traditional measurements rely on interviews or questionnaires, which are labor-intensive, context-specific, and unsuitable for continuous monitoring. Consequently, leaders lack practical tools to track shared cognition in real time. This paper’s empirical analysis shows that only specific categories (e.g., informative exchanges) correlate strongly with SMM, clarifying which forms of communication can influence shared cognition. This insight leads to our proposed approach, which estimates SMM from instant messaging systems like Slack. Our approach categorizes messages into communicative acts using large language models, constructs category-wise communication graphs, and applies a graph neural network for estimation. The model outperforms baselines, demonstrating the feasibility of continuous, scalable monitoring without intrusive surveys. While validated in corporate contexts, the approach extends to education, healthcare, and disaster response domains.