Marcia Sprang

Abstract. We have explored using neurophysiologic collaboration patterns as an approach for developing a deeper understanding of how teams collaborate when solving time-critical, complex real-world problems. Teams of three students solved substance abuse management simulations using IMMEX software while measures of mental workload (WL) and engagement (E) were generated by electroencephalography (EEG). Levels of high and low workload and engagement were identified for each member at each epoch statistically and the vectors consisting of these measures were clustered by self organizing artificial neural networks. The resulting cognitive teamwork patterns, termed neural synchronies, were different across six different teams. When the neural synchronies were compared across the team members of individual teams segments were identified where different synchronies were preferentially expressed. Some were expressed early in the collaboration when the team members were forming mental models of the problem, others were expressed later in the collaboration when the team members were sharing their mental models and converging on a solution. These studies indicate that non-random patterns of neurophysiologic synchronies can be observed across teams and members of a team when they are engaged in problem solving. This approach may provide an approach for monitoring the quality of team work during complex, real-world and possible one of a kind problem solving