Team mental models (TMMs) are the representations of shared cognition within a group, driving team coordination based on their shared understanding of critical information. TMM accuracy is traditionally operationalized as the structural similarity between the TMM and an expert's mental model across a fixed set of task-relevant concepts. However, this approach to TMM accuracy does not capture information accuracy, as teams may discuss topics outside the scope of the expert’s mental model. The present dissertation introduces a new methodology for extracting TMMs from team transition phases and evaluating their accuracy against a referent model. Semantic meaning is extracted from team communications using the DeBERTa neural transformer architecture, which is then represented as a topic network through Exploratory Graph Analysis (EGA). Finally, this team topic network is compared for similarity to a referent network based on node-level and tie-level properties through a technique called neural graph matching, which leverages a graph neural network (GNN) to compare networks that differ in size and content. The present study used this technique to derive each team's TMM focus (i.e., the proportion of on-task to off-task knowledge in the team discussion) and TMM completeness (i.e., the extent to which all topics in the referent network are represented in the team's network). Results from the neural graph matching positively correlated with expert judgments, presenting initial evidence of the technique's validity. TMM focus showed generally null relationships with performance. Teams showed improved performance on tasks where they achieved higher TMM completeness (within-team) compared to their average completeness over all tasks. Unexpectedly, teams lower in average completeness demonstrated an improved trajectory in performance over time compared to teams with higher average completeness. Finally, the moderating role of the rate of change for TMM completeness within a transition phase yielded null results. Overall, these results suggest that TMM information accuracy has a dynamic relationship with performance, as teams monitor and adapt their strategies over time to achieve high performance. Further, this dissertation presents initial validity evidence for a new technique to estimate the information accuracy of TMMs. These new methods provide a foundation for measuring information accuracy in TMMs, and can clarify the mechanisms driving the dynamic cycle between team transition and action phases.
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