Advertisement

Behavior Research Methods

, Volume 51, Issue 1, pp 384–397 | Cite as

Validating team communication data using a transmission-duration threshold and voice activity detection algorithm

  • Simon G. HoskingEmail author
  • Christopher J. Best
  • Dawei Jia
  • Peter Ross
  • Patrick Watkinson
Article

Abstract

The processes underlying team effectiveness can be understood by analyzing the temporal dynamics of team communication sequences. The results of such analyses have shown that the complexity of team communication is associated with team performance on task-related variables, and hence communication complexity statistics have been proposed for use as measures for real-time feedback on team performance. In two analyses of historical team communication sequences, we found that filtering via use of a transmission-duration threshold and voice activity detection algorithm resulted in significant changes in complexity relative to not filtering the data or using a transmission-duration filter alone. The use of these filtering techniques showed significant effects on the complexity of communication sequences in both a laboratory-based experiment, with participants with little experience with voice communication protocols, and in a mission simulation with trained military operators. There was also a significant non-linear relationship between the complexity of communication sequences and task performance. However, an analysis of the impact of the changes in communication dynamics gained through filtering did not demonstrate that the changed temporal dynamics of filtered data better explained team performance. It is concluded that pre-filtering of invalid communication data should be included during the data cleaning stage of statistical analysis as a matter of good scientific practice. Furthermore, such use of filtering will ensure that inferences made about the relationship between the complexity of communication between team members and their performance are not confounded by the presence of invalid communication events.

Keywords

Team communication Sample entropy Communication sequence filtering 

Notes

References

  1. Bernstein, N.A. (1996). Resources for ecological psychology. Dexterity and its development. In M.L. Latash, & M.T. Turvey (Eds.) Hillsdale: Lawrence Erlbaum Associates, Inc.Google Scholar
  2. Bessette, B., Salami, R., Lefebvre, R., & et al. (2002). The adaptive multirate wideband speech codec (AMR-WB). IEEE Transactions on Speech and Audio Processing, 10, 620–636.  https://doi.org/10.1109/TSA.2002.804299 CrossRefGoogle Scholar
  3. Coco, M.I., & Dale, R. (2014). Cross-recurrence quantification analysis of categorical and continuous time series: An R package. Frontiers in psychology, 5, 510.  https://doi.org/10.3389/fpsyg.2014.00510 CrossRefGoogle Scholar
  4. Cooke, N.J., Duchon, A., Gorman, J.C., Keyton, J., & Miller, A. (2012a). Preface to the special section on methods for the analysis of communication. Human Factors, 54, 485–488.  https://doi.org/10.1177/0018720812448673 CrossRefGoogle Scholar
  5. Cooke, N.J., Gorman, J.C., Myers, C., & Duran, J. (2012b). Theoretical underpinnings of interactive team cognition. In E. Salas, S. M. Fiore, & M.P. Letsky (Eds.) New York: Routledge.Google Scholar
  6. Cooke, N.J., Gorman, J.C., Myers, C.W., & Duran, J.L. (2013). Interactive team cognition. Cognitive science, 37, 255–285.CrossRefGoogle Scholar
  7. Francis, C., Best, C., & Yildiz, J. (2015). Improving air force operator performance through synthetic mission rehearsal. In Proceedings of SimTecT 2015.Google Scholar
  8. Gontar, P., Fischer, U., & Bengler, K. (2017). Methods to evaluate pilots’ cockpit communication: Cross-recurrence analyses vs. speech act-based analyses. Journal of Cognitive Engineering and Decision Making, 11 (4), 337–352.CrossRefGoogle Scholar
  9. Gorman, J.C., Cooke, N.J., Amazeen, P.G., & Fouse, S. (2012). Measuring patterns in team interaction sequences using a discrete recurrence approach. Human Factors, 54, 503–517.CrossRefGoogle Scholar
  10. Gorman, J.C., Martin, M.J., Dunbar, T.A., Stevens, R.H., Galloway, T.L., Amazeen, P.G., & Likens, A.D. (2016). Cross-level effects between neurophysiology and communication during team training. Human factors, 58, 181–199.  https://doi.org/10.1177/0018720815602575 CrossRefGoogle Scholar
  11. Guastello, S.J. (1998a). Creative problem solving groups at the edge of chaos. The Journal of Creative Behavior, 32(1), 38–57.CrossRefGoogle Scholar
  12. Guastello, S.J., Hyde, T., & Odak, M. (1998b). Symbolic dynamic patterns of verbal exchange in a creative problem solving group. Nonlinear Dynamics. Psychology, and Life Sciences, 2(1), 35–58.Google Scholar
  13. Guastello, S.J. (2000). Symbolic dynamic patterns of written exchanges: Hierarchical structures in an electronic problem solving group. Nonlinear Dynamics, Psychology, and Life Sciences, 4(2), 169–187.CrossRefGoogle Scholar
  14. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.Google Scholar
  15. Ishak, A.W., & Ballard, D.I. (2012). Time to re-group: A typology and nested phase model for action teams. Small Group Research, 43, 3–29.CrossRefGoogle Scholar
  16. Kozlowski, S.W., & Ilgen, D.R. (2006). Enhancing the effectiveness of work groups and teams. Psychological science in the public interest, 7, 77–124.CrossRefGoogle Scholar
  17. MacMillan, J., Entin, E.B., Hess, K.P., & Paley, M.J. (2004). Measuring performance in a scaled world: Lessons learned from the distributed dynamic decision making (DDD) synthetic team task. Scaled Worlds: Development, Validation, and Applications. Ashgate Publishing Co.Google Scholar
  18. Marks, M.A., Mathieu, J.E., & Zaccaro, S.J. (2001). A temporally based framework and taxonomy of team processes. The Academy of Management Review, 23, 356–376.CrossRefGoogle Scholar
  19. Murase, T., Poole, M.S., Asencio, R., & McDonald, J. (2017). Sequential synchronization analysis. In Group processes (pp. 119–144). Springer.Google Scholar
  20. Parker, J., Best, C.J., Funke, G., Strang, A., & Marion, K. (2016). An investigation of coding schemes for sample entropy analysis of communications data. In HFES 2016 : Proceedings of the 56th Human Factors and Ergonomics Society Annual Meeting (pp. 111–115).Google Scholar
  21. Richman, J.S., & Moorman, J.R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278, H2039–H2049.CrossRefGoogle Scholar
  22. Russell, S.M., Funke, G.J., Knott, B.A., & Strang, A.J. (2012). Recurrence quantification analysis used to assess team communication in simulated air battle management. In HFES 2012 : Proceedings of the 56th Human Factors and Ergonomics Society Annual Meeting (pp. 468–472).Google Scholar
  23. Sokunbi, M.O. (2014). Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets. Frontiers in Neuroinformatics, 8, 1–12.  https://doi.org/10.3389/fninf.2014.00069 CrossRefGoogle Scholar
  24. Stergiou, N., Harbourne, R., & Cavanaugh, J. (2006). Optimal movement variability: A new theoretical perspective for neurologic physical therapy. Journal of Neurologic Physical Therapy, 30, 120–129.CrossRefGoogle Scholar
  25. Stevens, R.H., Galloway, T.L., Wang, P., & Berka, C. (2012). Cognitive neurophysiologic synchronies: what can they contribute to the study of teamwork? Human Factors, 54, 489–502.  https://doi.org/10.1177/0018720811427296 CrossRefGoogle Scholar
  26. Strang, A.J., Horwood, S., Best, C.J., Funke, G.J., Knott, B.A., & Russell, S.M. (2012). Examining temporal regularity in categorical team communication using sample entropy. In HFES 2012 : Proceedings of the 56th Human Factors and Ergonomics Society Annual Meeting.Google Scholar
  27. Watkinson, P., Best, C., Von Trevor, K., & Jia, D (2015). An investigation of the directional relationship between cohesiveness and action team performance. In Proceedings of the Australian Psychological Society Industrial and Organisational Psychology conference.Google Scholar
  28. Weippert, M., Behrens, M., Rieger, A., & Behrens, K. (2014). Sample entropy and traditional measures of heart rate dynamics reveal different modes of cardiovascular control during low intensity exercise. Entropy, 16, 5698–5711.CrossRefGoogle Scholar
  29. Wiltshire, T.J., Butner, J.E., & Fiore, S.M. (2018). Problem-solving phase transitions during team collaboration. Cognitive Science, 42(1), 129–167.CrossRefGoogle Scholar
  30. Yentes, J.M., Hunt, N., Schmid, K.K., Kaipust, J.P., McGrath, D., & Stergiou, N. (2013). The appropriate use of approximate entropy and sample entropy with short data sets. Annals of Biomedical Engineering, 41, 349–365.  https://doi.org/10.1007/s10439-012-0668-3 CrossRefGoogle Scholar
  31. Zhang, X.S., & Roy, R.J. (2001). Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Transactions on Biomedical Engineering, 48, 312–323.CrossRefGoogle Scholar

Copyright information

© Her Majesty the Queen in Right of Australia 2018

Authors and Affiliations

  • Simon G. Hosking
    • 1
    Email author
  • Christopher J. Best
    • 1
  • Dawei Jia
    • 1
  • Peter Ross
    • 1
  • Patrick Watkinson
    • 2
  1. 1.Department of Defence, Aerospace DivisionDefence Science and TechnologyPort MelbourneAustralia
  2. 2.Department of PsychologyDeakin UniversityBurwoodAustralia

Personalised recommendations