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Measuring Patterns in Team Interaction Sequences Using a Discrete Recurrence Approach

2011; SAGE Publishing; Volume: 54; Issue: 4 Linguagem: Inglês

10.1177/0018720811426140

1547-8181

Jamie C. Gorman, Nancy J. Cooke, Polemnia G. Amazeen, Shannon Fouse,

Neural Networks and Applications

Objective: Recurrence-based measures of communication determinism and pattern information are described and validated using previously collected team interaction data. Background: Team coordination dynamics has revealed that “mixing” team membership can lead to flexible interaction processes, but keeping a team “intact” can lead to rigid interaction processes. We hypothesized that communication of intact teams would have greater determinism and higher pattern information compared to that of mixed teams. Method: Determinism and pattern information were measured from three-person Uninhabited Air Vehicle team communication sequences over a series of 40-minute missions. Because team members communicated using push-to-talk buttons, communication sequences were automatically generated during each mission. Results: The Composition × Mission determinism effect was significant. Intact teams’ determinism increased over missions, whereas mixed teams’ determinism did not change. Intact teams had significantly higher maximum pattern information than mixed teams. Conclusion: Results from these new communication analysis methods converge with content-based methods and support our hypotheses. Application: Because they are not content based, and because they are automatic and fast, these new methods may be amenable to real-time communication pattern analysis.