A Linguistic Analysis of the Impact of Team Interactions on Team Performance During Space Exploration Missions

This paper examines the impact of team interaction on team performance via linguistic content analysis. Linguistic markers expressed during the interaction are associated with self-assessed team performance via a linear mixed-effect model (LME) in the presence or absence of subtle interactions called “micro-behaviors”. We applied the LME analysis to longitudinal data collected from 9 teams participating in a 45-day simulated spaceflight mission in a ground analog. More specifically we used data collected from team interaction battery (TIB) tasks teams performed during missions which comprise around on average 1.5 hours of conversation data for 5 days per team. The same data was used to predict self-assessed team performance using a deep learning model based on a graph neural network (GNN) to capture interaction patterns between members and a recurrent neural network (RNN) to capture the history of conversation among speakers. Results obtained via an ablation study suggested that the proposed model significantly outperforms the baseline models that modeled either the group interaction between team members or the sequential nature of the data. The study indicates that interaction dynamics among team members are important for the automatic assessment of team performance, thereby informing the development of personalized interventions with concrete and tailored action items to mitigate team performance degradation.