Subsequent refinements have incorporated temporal components, nonlinear dynamical extensions and quantitative metrics. The generation of heterologous multi-team models and linkage of them with team member speech frequencies has allowed comparisons across teams, training sessions and levels of experience (Stevens, R. H., Galloway, T., Wang, P. & Berka, C., 2012; Stevens, R.H., Gorman, J.C., Amazeen, P., Likens, A. & Galloway, T., 2013; Stevens, R. H. & Galloway, T., 2014; Likens, A. D., Amazeen, P. G.; Stevens, R., Galloway, T., & Gorman, J. C., 2014). The models developed are reliable, sensitive and valid indicators of the changing neurodynamics of teams around which standardized quantitative models can begin to be developed. The technology is intended for documenting how rapidly teams are progressing towards proficiency and expertise and for understanding why some teams function better than others.

Like most forms of social coordination, teamwork is complicated, complex, and noisy. It is complicated as teams generally form around tasks that are too difficult for individuals to accomplish alone and require a diversity of experience and expertise. It is complex in the circular causality and feedback among multiple systems and sub-systems involved. For instance, neurophysiological events give rise to speech and other forms of inter-person communications which in turn affect subsequent speech and behavior. It is also complex in the sense that behaviors emerge in teams that often could not be predicted beforehand; i.e., the whole can be greater than the sum of its parts. Finally, teams are noisy in the sense that as the team develops consensus, many actions may occur that are peripheral to the immediate task.

These properties of being complicated, complex, and noisy pose challenges for evaluating teams, and at some point, seemingly simple questions like “How is this team doing?” become difficult to answer, particularly if the goal is to capture quantitative measures of team improvement over time. Part of the challenge is that unlike the performance evaluations of individuals, there are few measures and models for rapidly comparing across teams. This is particularly true with teams of diverse experience, who are performing real-world tasks where errors may be infrequent and do not directly correspond to failure (Schmidt, Keeton, Slack, Leveton, & Shea, 2009).

We have proposed that neurodynamics may provide a platform for developing quantitative models of team organization and perhaps performance (Stevens, 2012; Stevens, Galloway, Wang, & Berka, 2012). It is not surprising that neurophysiologic activities are the underpinnings of the social coordination dynamics described above, yet it is only recently that their evolving dynamics in real-world teamwork settings have begun to be modeled (Dodel et al., 2011; Dumas, Nadal, Soussignan, Martinerie & Garnero, 2010; Stephens, Silbert, & Hasson, 2010; Stevens, Galloway, Berka, & Sprang, 2009).