Approaches for Inserting Neurodynamics into the Training of Healthcare Teams

Part of the Cognitive Science and Technology book series (CSAT)


Team neurodynamics is the study of the changing rhythms and organizations of teams from the perspective of neurophysiology. As a discipline, team neurodynamics is located at the intersection of collaborative learning, psychometrics, complexity theory, and neurobiology with the resulting principles and applications both drawing from and contributing to these specialties. This article describes the tools for studying team neurodynamics and illustrates the potential and the challenges these methods and models have for better understanding healthcare team training and performance. The fundamental metric is neurodynamic organization, which is the tendency of teams and its members to enter into prolonged metastable relationships when they experience and resolve uncertainty. The patterns of these relationships are resolved by symbolic modeling of electroencephalographic (EEG) power levels of the team members, and the information in these patterns are calculated using information theory tools. The topics discussed in this chapter anticipate the time when dynamic biometric data can contribute to our understanding of how to rapidly determine a team’s functional status, and how to use this information to optimize outcomes and training. The rapid, dynamic, and task neutral measures make the lessons learned in healthcare applicable to other complex group and team environments, and provide a foundation for incorporating these models into machines to support the training and performance of teams.


Team neurodynamics EEG Debriefing Entropy Information theory 


  1. Abersold, M., (2018). Simulation-based learning: No longer a novelty in undergraduate education. OJIN: The Online Journal of Issues in Nursing 23(2).
  2. Ancona, D., & Chong, C. L. (1999). Cycles and synchrony: The temporal role of context in team behavior. Research on Managing Groups and Teams, 2, 33–48.Google Scholar
  3. Anders, S., Heinzle, J., Weiskopf, N., Ethofer, T., & Haynes, J. (2011). Flow of affective information between communicating brains. Neuroimage, 54, 439–446.CrossRefGoogle Scholar
  4. Baker, D. P., Amodeo, A. M., Krokos, K. J., Slonim, A., & Herrera, H. (2010). Assessing teamwork attitudes in healthcare: Development of the TeamSTEPPS® teamwork attitudes questionnaire. Quality and Safety Health Care, 19, e49.Google Scholar
  5. Bond, W. F., Deitrick, L. M., Eberhardt, M., et al. (2006). Cognitive versus technical debriefing after simulation training. Academy of Emergency Medicine, 13, 276–283.CrossRefGoogle Scholar
  6. Bonnefond, M., & Jensen, O. (2015). Gamma activity coupled to alpha phase as a mechanism for top-down controlled gating. PLoS ONE, 10, e012866.CrossRefGoogle Scholar
  7. Bowe, S., Johnson, K., & Puscas, L. (2017). Facilitation and debriefing in simulation education. Otolaryngologic Clinics of North America, 50(5), 989–1001.CrossRefGoogle Scholar
  8. Buzaki, G. (2006). Rhythms of the brain. New York, NY: Oxford University Press.CrossRefGoogle Scholar
  9. Cheng, A., Morse, K. J., Rudolph, J., Arab, A., Runnacles, J., & Eppich, W. (2016). Learner-centered debriefing for health care simulation education: Lessons for faculty development. Simulation in Healthcare, 11, 32–40.CrossRefGoogle Scholar
  10. Clapper, T. C. (2016). Proposing a new debrief checklist for team STEPPS to improve documentation and clinical debriefing. Simulation and Gaming, 47, 710–719.CrossRefGoogle Scholar
  11. Cooke, N. J., Gorman, J. C., & Kiekel, P. (2008). Communication as team-level cognitive processing. In M. P. Letsky, N. W. Warner, S. M. Fiore & C. A. P. Smith (Eds.), Macrocognition in teams. Ashgate: Burlington, VT, USA.Google Scholar
  12. Cooke, N. J., Salas, E., Cannon-Bowers, J., & Stout, R. (2000). Measuring team knowledge. Human Factors, 42, 151–173.CrossRefGoogle Scholar
  13. Daw, C. S., Finney, C. E. A., & Tracy, E. R. (2003). A review of symbolic analysis of experimental data. Review of Scientific Instruments, 74, 915. Scholar
  14. Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S. (2012). Independent EEG sources are dipolar. PLoS ONE, 7(2), e30135.CrossRefGoogle Scholar
  15. Draschkow, D., Heikel, E., Võ, M., Fiebach, C. J., & Sassenhagen, J., (2018). No evidence from MVPA for different processes underlying the N300 and N400 incongruity effects in object-scene processing. Neuropsychologia, 120, 9–17. ISSN 0028-3932.
  16. Entin, E. E., & Serfaty, D. (1999). Adaptive team coordination. Human Factors: The Journal of the Human Factors and Ergonomics Society, 41(2), 312–325.CrossRefGoogle Scholar
  17. Fanning, R., & Gaba, D. (2007). The role of debriefing in simulation-based learning. Simulation in Healthcare, 2, 115–125.CrossRefGoogle Scholar
  18. Farooqui, A. A., & Manly, T. (2018). Hierarchical cognition caused task related deactivations but not just in default mode regions. eNeuro, 5(6), ENEURO.0008-18.2018.Google Scholar
  19. Fiore, S. M., Smith-Jentsch, K. A., Salas, E., Warner, N., & Letsky, M. (2010). Towards an understanding of macro cognition in teams: Developing and defining complex collaborative process and product. Theoretical Issues in Ergonomics Science, 11(4), 250–271.CrossRefGoogle Scholar
  20. Flack, J. (2017). Life’s information hierarchy. In S. I. Walker, P. C. W. Davies & G. F. R. Ellis (Eds.), From matter to life: Information and causality. Cambridge: Cambridge University Press.Google Scholar
  21. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11, 127–138. Scholar
  22. Gardezi, F., Lingard, L., Espin, S. L., Whyte, S., Orser, B., & Baker, G. R. (2009). Silence, power and communication in the operating room. Journal of Advanced Nursing, 65, 1390–1399.CrossRefGoogle Scholar
  23. Garner, A. J. P., Thompson, J., Vedral, V., & Gu, M. (2017, April 16). Thermodynamics of complexity and pattern manipulation. arXiv:1510:00010v3[].
  24. Giere, R. N., & Moffatt, B. (2003). Distributed cognition: Where the cognitive and social merge. Social Studies of Science, 33, 301–310.CrossRefGoogle Scholar
  25. Gorman, J. C. (2014). Team coordination and dynamics: Two central issues. Current Directions in Physiological Science, 23, 355–360.CrossRefGoogle Scholar
  26. Grimm, D., Gorman, J. C., Stevens, R. H., Galloway, T., Willemsen-Dunlap, A. M., & Halpin, D. J. (2017). Demonstration of a method for real-time detection of anomalies in team communication. In Proceedings of the Human Factors and Ergonomics Society 59th Annual Meeting (pp. 282–286). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
  27. Guastello, S. J., & Peressini, A. F. (2018). Development of a synchronization coefficient for biosocial interactions in groups and teams. Nonlinear Dynamics, Psychology, Life Sciences, 48, 3–33.Google Scholar
  28. Hari, R. (2006). Action perception connection and the cortical mu-rhythm. Progress in Brain Research, 159, 253–260.CrossRefGoogle Scholar
  29. Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F. (2005). Neural systems responding to degrees of uncertainty in human decision making. Science, 310, 1680–1683.CrossRefGoogle Scholar
  30. Husebo, S. E., Dieckmann, P., Rystedt, H., Soreide, E., & Friberg, F. (2013). The relationship between facilitators’ questions and the level of reflection in postsimulation debriefing. Simulation in Healthcare, 8, 135–141.CrossRefGoogle Scholar
  31. James, R. G., Ellison, C. J., & Crutchfield, J. P. (2011). Anatomy of a bit: Information in a time series observation (Santa Fe Institute Working Paper 11–05). arXiv:1105.2988v1[cs.IT].
  32. Kihlgren, P., Spanger, L., & Dieckmann, P. (2014). Investigating novice doctors’ reflections in debriefings after simulation scenarios. Journal of Medical Teacher, 37, 437–443.CrossRefGoogle Scholar
  33. Kolchinsky, A., & Wolpert, D. H. (2018). Semantic information, autonomous agency and non-equilibrium statistical physics. Interface Focus, 8, 20180041.CrossRefGoogle Scholar
  34. Lachaux, J. P., Jung, J., Dreher, J. C., Bertrand, O., Minotti, L., Hoffman, D., et al. (2008). Silence is golden: Transient neural deactivation in the prefrontal cortex during attentive reading. Cerebral Cortex, 18, 443–450.CrossRefGoogle Scholar
  35. Lederman, L. (1992). Debriefing: toward a systemic assessment of theory and practice. Simulation and Gaming, 23, 145–160.CrossRefGoogle Scholar
  36. Marks, M., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally-based framework and taxonomy of teamwork. The Academy of Management Review, 26, 356–376.CrossRefGoogle Scholar
  37. Menoret, M., Varnet, L., Fargier, R., Cheylus, A., Curie, A., des Portes, V., et al. (2014). Neural correlates of non-verbal social interactions: A dual-EEG study. Neurophyschologia, 55, 85–91.CrossRefGoogle Scholar
  38. Mosier, K. H., & Chidester, T. R. (1991). Situation assessment and situation awareness in a team setting. In Y. Queinnec & F. Daniellou (Eds.), Designing for everyone (pp. 798–800). London: Taylor & Francis.Google Scholar
  39. Mullen, T. (2012). NITRC: CleanLine: Tool/Resource Info. Retrieved from
  40. Murray, E. A., & Rudebeck, P. H. (2017). Specializations for reward-guided decision-making in the primate ventral prefrontal cortex. Nature Reviews Neuroscience, 19, 404–417.CrossRefGoogle Scholar
  41. Oostenveld, R., Fries, P., Maris, E., & Schoffelen, M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, Article ID 156869, 9 pp. Scholar
  42. Ossandon, T., Jerbi, K., Vidal, J. R., Bayle, D. J., Henaff, M.-A., Jung, J., et al. (2011). Transient suppression of broadband gamma power in the default mode network is correlated with task complexity and subject performance. Journal of Neuroscience, 31, 14521–14530.CrossRefGoogle Scholar
  43. O’Neil, H. F., Jr., Chung, G. K., & Brown, R. (1997). Use of networked simulations as a context to measure team competencies. In H. F. O’Neil Jr. (Ed.), Workforce readiness: Competencies and assessment (pp. 411–452). Mahwah, NJ: Erlbaum.Google Scholar
  44. Petranek, C. (1994). Maturation in experiential learning: Principles of simulation and gaming. Simulation in Gaming, 25, 513–522.CrossRefGoogle Scholar
  45. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of National Academy of Science of USA, 98, 676–682.Google Scholar
  46. Salas, E., Stagl, K.C., & Burke, C. S. (2004). 25 Years of team effectiveness in organizations: Research themes and emerging needs. In C. L. Cooper & I. T. Robertson (Eds.), International review of industrial and organizational psychology (Vol. 19, pp. 56–101). New York: Wiley.Google Scholar
  47. Salas, E., Stevens, R., Gorman, J., Cooke, N. J., Guastello, S., & von Davier, A. (2015, September). What will quantitative measures of teamwork look like in 10 years? In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59 (1), 235–239 (Sage Publications).Google Scholar
  48. Sanger, J., Muller, V., & Lindenberger, U. (2012, November). Intra-and interbrain synchronization and network properties when playing guitar in duets. Frontiers in Human Neuroscience, Article 312.
  49. Sato, N., & Mizuhara, H. (2018). Successful encoding during natural reading is associated with fixation related potential and large-scale network activation. eNeuro. Scholar
  50. Sawyer, T., & Deering, S. (2013). Adaptation of the US Army’s after-action review for simulation debriefing in healthcare. Simulation in Healthcare, 8, 388–397.CrossRefGoogle Scholar
  51. Schippers, M., Roebroeck, A., Renken, R., Nanetti, L., & Keysers, C. (2010). Mapping the information flows from one brain to another during gestural communication. Proceedings of National Academy of Science of USA, 107, 9388–9393.Google Scholar
  52. Schmidt, E., Goldhaber-Fiebert, S. M., Ho, L. A., & McDonald, D. (2013). Simulation exercises as a patient safety strategy: A systematic review. Annals of Internal Medicine, 158, 426–432.CrossRefGoogle Scholar
  53. Sedley, W., & Cunningham, M. O. (2013). Do cortical gamma oscillations promote or suppress perception? An under-asked question with an over-assumed answer. Frontiers in Human Neuroscience. 7, Article 595.Google Scholar
  54. Shockley, K., Santana, M.-V., & Fowler, C. A. (2003). Mutual interpersonal postural constraints are involved in cooperative conversation. Journal of Experimental Psychology: Human Perception and Performance, 29, 326–332.Google Scholar
  55. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423.MathSciNetzbMATHCrossRefGoogle Scholar
  56. Shannon, C. E. (1951). Prediction and entropy of printed English. The Bell System Technical Journal, 30, 50–64.zbMATHCrossRefGoogle Scholar
  57. Staropoli, P. C., Gregori, N. Z., Junk, A. K., Galor, A., Goldhardt, R., Goldhagen, B. E., et al. (2018). Surgical simulation training reduces intraoperative cataract surgery complications among residents. Simulation in Healthcare, 13, 11–15. Scholar
  58. Stephens, G., Silbert, L., & Hasson, U. (2010). Speaker-listener neural coupling underlies successful communication. Proceedings of the National academy of Sciences of the United States of America. Retrieved from
  59. Stevens, R. H., & Galloway, T. (2014). Toward a quantitative description of the neurodynamic organizations of teams. Social Neuroscience, 9(2), 160–173.CrossRefGoogle Scholar
  60. Stevens, R. H., & Galloway, T. (2015). Modeling the neurodynamic organizations and interactions of teams. Social Neuroscience, 11, 123–139. Scholar
  61. Stevens, R. H., & Galloway, T. (2017, May). Are neurodynamic organizations a fundamental property of teamwork? Frontiers in Psychology.
  62. Stevens, R., & Galloway, T. L. (2019). Teaching machines to recognize neurodynamic correlates of team and team member uncertainty. Journal of Cognitive Engineering and Decision Making, 13, 310–327. Scholar
  63. Stevens, R. H., Galloway, T., Halpin, D., & Willemsen-Dunlap, A. (2016). Healthcare teams neurodyamically reorganize when resolving uncertainty. Entropy, 18, 427. Scholar
  64. Stevens R. H., Galloway T., Lamb J., Steed R., & Lamb C. (2017). Linking team neurodynamic organizations with observational ratings of team performance. In A. von Davier, M. Zhu & P. Kyllonen (Eds.), Innovative assessment of collaboration. New York, NY: Springer.Google Scholar
  65. Stevens, R.H., Galloway, T., & Willemsen-Dunlap, A., (2016). Intermediate neurodynamic representations: A pathway towards quantitative measurements of teamwork? Human Factors & Ergonomics National Meeting, 60(1), 1996–2000.Google Scholar
  66. Stevens, R. H., Galloway, T. & Willemsen-Dunlap, A. (2017). Low level predictors of team dynamics: A neurodynamic approach. In E. Salas, B. Vessey & L. B. Landon (Eds.), Research in managing groups and teams. Research series: Team dynamics over time, Vol. 18, pp. 71–92. Emerald Insight.Google Scholar
  67. Stevens, R.H., Galloway, T., & Willemsen-Dunlap. (2018). Quantitative modeling of individual, shared and team neurodynamic information. Human Factors, 60, 1022–1034.Google Scholar
  68. Stevens, R.H., Galloway, T., Gorman, J., Willemsen-Dunlap, A., Halpin, D., & Grimm, D. (2018, October). Making sense of team information. Paper presented at 2018 Human Factors & Ergonomics National Meeting, Philadelphia, PA.Google Scholar
  69. Stevens, R., Galloway, T. L., & Willemsen-Dunlap, A. (2019). Advancing our understandings of healthcare team dynamics from the simulation room to the operating room: A neurodynamic perspective. Frontiers in Psychology, 10, 1660.
  70. Tognoli, E., & Kelso, J. A. (2015). The coordination dynamics of social neuromarkers. Frontiers of Human Neuroscience, 9, 563. Scholar
  71. Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews in Neuroscience, 17, 450–461.CrossRefGoogle Scholar
  72. Vaskinn, A., Sergi, M. J., & Green, M. F. (2009). The challenges of ecological validity in the measurement of social perception in schizophrenia. The Journal of Nervous and Mental Disease, 197, 700–702.CrossRefGoogle Scholar
  73. Vergauwe, E., & Cowan, N. (2014, March). A common short-term memory retrieval rate may describe many cognitive procedures. Frontiers of Human Neuroscience, Article 126.
  74. von Davier, A. A., & Halpin, P. F. (2013). Collaborative problem solving and the assessment of cognitive skills: Psychometric considerations. ETS Research Report Series, 2013(2), 1–36. Scholar
  75. Zenati, M. A., Leissner, K. B., Zorca, S., Kennedy-Metz, L., Yule, S., & Dias, R. (2019). First reported use of team cognitive workload for root cause analysis in cardiac surgery. Seminar Thoracic Surgery.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.UCLA School of MedicineBrain Research InstituteLos AngelesUSA
  2. 2.The Learning Chameleon, Inc.Culver CityUSA
  3. 3.JUMP Simulation and Education CenterPeoriaUSA

Personalised recommendations