In this presentation we will explore how we can begin to ‘make sense’ of the information in these data streams that are being recorded from teams at increasingly higher resolutions, and what can we expect to learn about teams through the application of different representations, transformations and aggregations of information.

The aim of this study was to use the same quantitative measure and scale to directly compare the neurodynamic information/organizations of individual team members with those of the team.

Objective: To determine whether a dynamical analysis of neural and communication data streams provide fine-grained insights into healthcare team debriefings.

The information within the neurodynamic data streams of teams engaged in naturalistic decision making was separated into information unique to each team member, the information shared by two or more team members, and team-specific information related to interactions with the task and team members.

In this chapter we highlight a neurodynamic approach that is showing promise as a quantitative measure of team performance.

We describe efforts to make humans more transparent to machines by focusing on uncertainty, a concept with roots in neuronal populations that scales through social interactions.  We are honored that this publication, was selected by HFES as the most outstanding paper in Journal of Cognitive Engineering and Decision during 2019. Special thanks to the editors and the reviewers for their detailed critique and suggestions.