About this Research Topic
A paradigm shift is underway for understanding how teams evolve and mature over time. It is being driven by the generation of neurobehavioral data and models, and their transfer from laboratory setting, to simulated reality (including physical environments with props), all the way to augmented/mixed reality and real-world scenarios within situated environments.
Combined with recent data analytics (e.g., machine learning, artificial intelligence), this provides a test-bed for empirical quantitative comparisons across teams, tasks, and expertise to help uncover fundamental principles of interactive cognition between team members both in real time during the evolving task, and after, during personal review or collective debriefing. This will enable cognitively-informed task designs and accelerate the rates of team and team-member learning by focusing on the cognitively-relevant properties. For example, tailoring training to focus on skills that still require high workload (and are thus not fully trained) could reduce training costs, accelerate training, and preserve and enhance training retention, thus increasing the likelihood of success on complex collective tasks. In addition, enabling cognitive assessment could detect when team members' cognitive states are becoming sub-optimal and alert them so that steps can be taken to optimize performance before their mission is endangered.
In addition, these technologies will likely shape the evolution of existing theories of teamwork and the development of richer computational methods to quantify the degree of team member coupling and describe the nature of information exchange. These advances will support the creation of new collective dynamic measures and guide how they can help develop new teaching practices while increasing the transparency of human states, enabling more effective machine-human collaborations.
The measures needed to instantiate these goals will likely share the properties of being:
· Dynamical with high temporal resolution;
· Quantitative where changes provide actionable information about the team and its team members;
· Scale-able so individual and team measures can be aggregated or deconstructed to span system scales;
· Domain neutral; and
· Predictive of possible changes in the team or the environment.
With such systems in place, important but difficult questions can be explored such as:
· What are key indicators to determine when a team and its team members are adequately (fully) trained such that they are able to operate effectively?
· How fast do individual skills and team processes decay and can neurodynamics hasten their acquisition and delay their decline?
· What are the neurodynamic origins of social-affective-cognitive states with particular relevance to team dynamics (i.e., trust, uncertainty, emotion, suspicion, physiological synchrony, team cohesion, and situation awareness in teams and their members)?
· How important are the above constructs for future forms of teamwork and how will we imbue machines with these capabilities?
This Research Topic, therefore, seeks papers that address the above topics, and others related to the theoretical, technical, and practice-oriented research into the neurodynamics of teams, and their linkages with other biometric and observable team behaviors.
Topic Editors Dr. Ron Stevens and Dr. Bethany Bracken are employed by The Learning Chameleon Inc and Charles River Analytics, respectively. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Combined with recent data analytics (e.g., machine learning, artificial intelligence), this provides a test-bed for empirical quantitative comparisons across teams, tasks, and expertise to help uncover fundamental principles of interactive cognition between team members both in real time during the evolving task, and after, during personal review or collective debriefing. This will enable cognitively-informed task designs and accelerate the rates of team and team-member learning by focusing on the cognitively-relevant properties. For example, tailoring training to focus on skills that still require high workload (and are thus not fully trained) could reduce training costs, accelerate training, and preserve and enhance training retention, thus increasing the likelihood of success on complex collective tasks. In addition, enabling cognitive assessment could detect when team members' cognitive states are becoming sub-optimal and alert them so that steps can be taken to optimize performance before their mission is endangered.
In addition, these technologies will likely shape the evolution of existing theories of teamwork and the development of richer computational methods to quantify the degree of team member coupling and describe the nature of information exchange. These advances will support the creation of new collective dynamic measures and guide how they can help develop new teaching practices while increasing the transparency of human states, enabling more effective machine-human collaborations.
The measures needed to instantiate these goals will likely share the properties of being:
· Dynamical with high temporal resolution;
· Quantitative where changes provide actionable information about the team and its team members;
· Scale-able so individual and team measures can be aggregated or deconstructed to span system scales;
· Domain neutral; and
· Predictive of possible changes in the team or the environment.
With such systems in place, important but difficult questions can be explored such as:
· What are key indicators to determine when a team and its team members are adequately (fully) trained such that they are able to operate effectively?
· How fast do individual skills and team processes decay and can neurodynamics hasten their acquisition and delay their decline?
· What are the neurodynamic origins of social-affective-cognitive states with particular relevance to team dynamics (i.e., trust, uncertainty, emotion, suspicion, physiological synchrony, team cohesion, and situation awareness in teams and their members)?
· How important are the above constructs for future forms of teamwork and how will we imbue machines with these capabilities?
This Research Topic, therefore, seeks papers that address the above topics, and others related to the theoretical, technical, and practice-oriented research into the neurodynamics of teams, and their linkages with other biometric and observable team behaviors.
Topic Editors Dr. Ron Stevens and Dr. Bethany Bracken are employed by The Learning Chameleon Inc and Charles River Analytics, respectively. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: Social Coordination, Team Neurodynamics, Information Theory, Cognitive Workload, Coupling, fNIRS, EEG
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
