Team Resilience: A Neurodynamic Perspective

  • Ronald Stevens
  • Trysha Galloway
  • Jerry Lamb
  • Ronald Steed
  • Cynthia Lamb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9183)

Abstract

Neurophysiologic models were created from US Navy navigation teams performing required simulations that captured their dynamic responses to the changing task environment. Their performances were simultaneously rated by two expert observers for team resilience using a team process rubric adopted by the US Navy Submarine Force. Symbolic neurodynamic (NS) representations of the 1−40 Hz EEG amplitude fluctuations of the crew were created each second displaying the EEG levels of each team member in the context of the other crew members and in the context of the task. Quantitative estimates of the NS fluctuations were made using a moving window of entropy. Periods of decreased entropy were considered times of increased team neurodynamic organization; e.g. when there were prolonged and restricted relationships between the EEG- PSD levels of the crew. Resilient teams showed significantly greater neurodynamic organization in the pre-simulation Briefing than the less resilient teams. Most of these neurodynamic organizations occurred in the 25−40 Hz PSD bins. In contrast, the more resilient teams showed significantly lower neurodynamic organization during the Scenario than the less resilient teams with the greatest differences in the 12−20 Hz PSD bins. The results indicate that the degree of neurodynamic organization reflects the performance dynamics of the team with more organization being important during the pre-mission briefing while less organization (i.e. more flexibility) important while performing the task.

Keywords

Team neurodynamics Resilience EEG Submarine 

References

  1. 1.
    Staal, M.E., Bolton, A., Yaroush, L.E., Bourne Jr., R.A.: Cognitive performance and resilience to stress. In: Lukey, B.J., Tepe, V. (eds.) Bio-behavioral Resilience to Stress, vol. 2. CRC Press, Taylor & Francis Group, London, UK (2009)Google Scholar
  2. 2.
    Carandini, M., Heeger, D.J.: Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2011). doi:10.1038/nrn3136 Google Scholar
  3. 3.
    Butner, J., Pasupathi, M., Vallejos, V.: When the facts just don’t add up: the fractal nature of conversational stories. Soc. Cognition 26, 670–699 (2008)CrossRefGoogle Scholar
  4. 4.
    Likens, A., Amazeen, P., Stevens, R., Galloway, T., Gorman, J.C.: Neural signatures of team coordination are revealed by multifractal analysis. Soc. Neurosci. 9(3), 219–234 (2014)CrossRefGoogle Scholar
  5. 5.
    Stevens, R.H., Gorman, J.C., Amazeen, P., Likens, A., Galloway, T.: The organizational dynamics of teams. Nonlinear Dyn. Psychol. Life Sci. 17(1), 67–86 (2013)Google Scholar
  6. 6.
    Stevens, R., Galloway, T., Wang, P., Berka, C.: Cognitive neurophysiologic synchronies: What can they contribute to the study of teamwork? Hum. Factors 54, 489–502 (2012)CrossRefGoogle Scholar
  7. 7.
    Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: an explanation of 1/f noise. Physical 59(4), 381–384 (1987)Google Scholar
  8. 8.
    Stevens, R.H., Galloway, T.: Toward a quantitative description of the neurodynamics organizations of teams. Soc. Neurosci. 9(2), 160–173 (2014)CrossRefGoogle Scholar
  9. 9.
    Hollnagel, E.: The four cornerstones of resilience engineering. In: Hollnagel, E., Dekker, S. (eds.) Resilience Engineering Perspectives. Preparation and Restoration, vol. 2, pp. 117–133. Ashgate, Farnham, UK (2009)Google Scholar
  10. 10.
    Hollnagel, E.: FRAM: The Functional Resonance Analysis Method. Modeling Complex Socio-Technical Systems. Ashgate, Aldershot (2012)Google Scholar
  11. 11.
    Wang, Y., Hong, B., Gao, X., Gao, S.: Design of electrode layout for motor imagery based brain-computer interface. Electron. Lett. 43(10), 557–558 (2007)CrossRefGoogle Scholar
  12. 12.
    Roux, F., Uhlhaas, P.: Working memory and neural oscillations: alpha-gamma versus theta-gamma codes for distinct WM information? Trends Cogn. Sci. 18, 16–25 (2014)CrossRefGoogle Scholar
  13. 13.
    Oberman, L.M., Pineda, J.A., Ramachandran, V.S.: The human mirror neuron system A link between action observation and social skills. Soc. Cogn. Affect. Neurosci. 2, 62–66 (2007)CrossRefGoogle Scholar
  14. 14.
    Berka, C., Levendowski, D.J., Cvetinovic, M.M., Petrovic, M.M., Davis, G., et al.: Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. Int. J. Hum. Comput. Interact. 17(2), 151–170 (2004)CrossRefGoogle Scholar
  15. 15.
    Daw, C.S., Finney, C.E.A., Tracy, E.R.: A review of symbolic analysis of experimental data. Rev. Sci. Instrum. 74, 915 (2003)CrossRefMATHGoogle Scholar
  16. 16.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series with implications for streaming algorithms. In: Proceedings of the 8th Data Mining and Knowledge Discovery, San Diego, CA (2003)Google Scholar
  17. 17.
    Shannon, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1949)MATHGoogle Scholar
  18. 18.
    Harmony, T.: The functional significance of delta oscillations in cognitive processing. Front. Integr. Neurosci. 7(83) (2013). doi:10.3389/fnint.2013.00083
  19. 19.
    Tognoli, E., Kelso, J.A.: The coordination dynamics of social neuromarkers (2013). Bibliographic Code: 2013arXiv1310.7275TGoogle Scholar
  20. 20.
    Klimesch, W., Sauseng, P., Hanslmayr, S.: EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res. Rev. 53, 63–88 (2007)CrossRefGoogle Scholar
  21. 21.
    Pineda, J.A.: Sensorimotor cortex as a critical component of an ‘extended’ mirror neuron system: does it solve the development, correspondence, and control problems in mirroring? Behav. Brain Funct. 4, 47–63 (2008)CrossRefGoogle Scholar
  22. 22.
    Menoret, M., Varnet, L., Fargier, R., Cheylus, A., Curie, A., desPortes, V., Nazir, T.A., Paulignan, U.: Neural correlates of non-verbal social interactions: a dual-EEG study. Neurophyschologia 55, 85–91 (2014)CrossRefGoogle Scholar
  23. 23.
    Caetano, G., Jousmaki, V., Hari, R.: Actor’s and observers primary motor cortices stabilize similarly after seen or heard motor actions. Proc. Nat. Acad. Sci. 104, 9058–9062 (2007)CrossRefGoogle Scholar
  24. 24.
    Woods, D., Hollnagel, E.: Resilience engineering concepts and Precepts. Ashgate Publishing, Burlington, VT, USA (2006)Google Scholar
  25. 25.
    Rankin, A., Lunderg, J., Woltjer, R., Rollenhagen, C., Hollnagel, E.: Resilience in everyday operations: a framework for analyzing adaptations in high-risk work. J. Cogn. Eng. Decis. Making 8(1), 78–97 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ronald Stevens
    • 1
    • 2
  • Trysha Galloway
    • 2
  • Jerry Lamb
    • 3
  • Ronald Steed
    • 4
  • Cynthia Lamb
    • 5
  1. 1.IMMEX/UCLALos AngelesUSA
  2. 2.The Learning Chameleon, Inc.Los AngelesUSA
  3. 3.Naval Submarine Medical Research LaboratoryGrotonUSA
  4. 4.UpScope Consulting, Inc.MysticUSA
  5. 5.URS Federal Services, Inc.San FranciscoUSA

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