Measuring patterns in team interaction sequences using a discrete recurrence approach

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

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)503-517
Number of pages15
JournalHuman Factors
Issue number4
StatePublished - Aug 2012


  • communication analysis
  • interaction analysis
  • pattern analysis
  • recurrence analysis
  • teamwork

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Applied Psychology
  • Behavioral Neuroscience


Dive into the research topics of 'Measuring patterns in team interaction sequences using a discrete recurrence approach'. Together they form a unique fingerprint.

Cite this