Ron Stevens

Temporal Sequences of Neurophysiologic Synchronies can Identify Changes in Team Cognition

Neurophysiologic synchronies (NS) are the second-by-second co-expression of the levels of cognitive measures by individual members of a team. Previously we showed that the NS obtained from EEG-derived measures of engagement (EEG-E) were not random across a variety of teamwork situations, but changed with changing task demands. In this study we hypothesized that the expression of different NS may represent unobserved states of the team and that the sequence of NS expression may contain long memory relevant to the performance of the team. To test this hypothesis we performed hidden Markov modeling of the EEG-E NS streams from novice and expert Navy submarine piloting and navigation teams and show that the dynamic expression of states derived from these models identified short and long-term changes in the behavior of teams.

SUBMARINE NAVIGATION TEAM RESILIENCE: LINKING EEG AND BEHAVIORAL MODELS

Advances in the assessment of submarine piloting and navigation teams have created opportunities for linking behavioral observations of team performances with neurodynamic measures of team organization, synchrony and change. Submarine navigation teams (n=12) were fitted with EEG headsets and recorded while conducting required navigation simulations. In parallel, their performances were assessed for team resilience by two evaluators using a team process rubric adopted by the Submarine Force. EEG models of team synchrony were created symbolically which identified times when there was increased across-team cognitive organization induced by the simulation and / or interactions with other crew members. One set of these organizations was observed in the 10 Hz EEG frequency band and coincided with the periodic activity of updating the ship’s position (e.g. Rounds). There were also periods of increased team synchrony between 25-40 Hz which were present during some Rounds events but were more prominent with task changes or when the team was stressed. More resilient teams had fewer periods of team synchrony and these were of smaller magnitude than those found in less resilient teams. These results indicate that both routine and unexpected activities trigger increased neurophysiologic synchrony / coherence in teams and that periods of persistent synchrony may signal a team being challenged.

Neurophysiologic Collaboration Patterns during Team Problem Solving

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.

Demonstration of a Method for Real-time Detection of Anomalies in Team Communication

Real-time analysis of team communication data to detect anomalies and/or perturbations in the team environment is an ideal method to improve on teams’ interactions and responses to potential crises. In this paper, we demonstrate a method to detect anomalies through observing communication patterns of neurosurgery teams. We simulated the real-time process by analyzing previously collected communication data to assess the effectiveness of a nonlinear prediction model to detect anomalies. We compared predicted values of communication determinism (a measure of how organized communication patterns are) to previous values in each team’s time series. These deviations formed a separate root mean square error (RMSE) time series, and we examined the magnitudes of the RMSE time series at the points of known perturbations. Additionally, we examined the effect of window size on perturbation detection. We found that our nonlinear prediction model accurately detected the perturbations and shows promise for future real-time analysis.

Assessing Student's Mental Representations of Complex Problem Spaces with EEG Technologies

We have developed a neurophysiologic-based assessment of students understanding of complex problem spaces that blends the population-based advantages of probabilistic performance modeling with the detection of neurophysiologic signals. It is designed to be rapid and effective in complex environments where assessment is often imprecise. Cohorts of novices, and experts encoded chemistry problem spaces by performing a series of online problem solving simulations. The stable memory encoding was verified by comparing their strategies with established probabilistic models of strategic performance. Then, we probed the neural correlates of the encoded problem space by measuring differential EEG signatures that were recorded in response to rapidly presented sequences of chemical reactions that represented different valid or invalid approaches for solving the chemistry problems. We found that experts completed performances in stacks more rapidly than did novices and they also correctly identified a higher percentage of reactions. Event related potentials revealed showed increased positivities in the 100–400 ms following presentation of the image preceding the decision when compared with the other stack images. This neural activity was used to explore reasons why students missed performances in the stack. One situation occurred when students appeared to have a lapse of attention. This was characterized by increased power in the 12–15 Hz range, a decrease in the ERP positivities at 100–400 ms after the final image presentation, and a slower reaction time. A second situation occurred when the students decisions were almost entirely the reverse of what were expected. These responses were characterized by ERP morphologies similar to those of correct decisions suggesting the student had mistaken one set of chemical reactions for another.

Low Level Predictors of Team Dynamics: A Neurodynamic Approach

Abstract nPurpose nIn this chapter we highlight a neurodynamic approach that is showing promise as a quantitative measure of team performance. n n nMethodology/approach nDuring teamwork the rapid electroencephalographic (EEG) oscillations that emerge on the scalp were transformed into symbolic data streams which provided historical details at a second-by-second resolution of how the team perceived the evolving task and how they adjusted their dynamics to compensate for, and anticipate new task challenges. Key to this approach are the different strategies that can be used to reduce the data dimensionality, including compression, abstraction and taking advantage of the natural redundancy in biologic signals. n n nFindings nThe framework emerging is that teams continually enter and leave organizational neurodynamic partnerships with each other, so-called metastable states, depending on the evolving task, with higher level dynamics arising from mechanisms that naturally integrate over faster microscopic dynamics. n n nPractical implications nThe development of quantitative measures of the momentary dynamics of teams is anticipated to significantly influence how teams are assembled, trained, and supported. The availability of such measures will enable objective comparisons to be made across teams, training protocols, and training sites. They will lead to better understandings of how expertise is developed and how training can be modified to accelerate the path toward expertise. n n nOriginality/value nThe innovation of this study is the potential it raises for developing globally applicable quantitative models of team dynamics that will allow comparisons to be made across teams, tasks, and training protocols.

Intermediate Neurodynamic Representations A Pathway towards Quantitative Measurements of Teamwork

We explored the possible linkages between expert observational ratings of team performance and the fluctuating neurodynamics of healthcare and submarine navigation teams while they conducted realistic training in natural settings. Second-by-second symbolic representations were created of team member’s electroencephalographic (EEG) power across the 1-40 Hz EEG spectrum, and quantitative estimates of the changing dynamics were calculated from the Shannon entropy of the data streams. Significant correlations were seen between the symbol streams entropy levels and ratings of team performance by observers using TeamSTEPPS® (healthcare), or Submarine Team Behavior Toolkit (submarine teams) rubrics. These results suggest that the frequency, magnitude, and / or durations of the teams’ neurodynamic fluctuations might reflect performance aspects detected by expert raters.

Towards the Development of Quantitative Descriptions of the Neurodynamic Rhythms and Organizations of Teams

The goal was to begin developing quantitative approaches for modeling the neurodynamic rhythms and organizations of teams. Raw EEG signals from team members were first transformed into es-timates of cognitive workload and transformed again into neurodynamics symbols showing the second-by-second workload of each individual as well as the team. Periods of increased or decreased symbol organiza-tion in the data streams were hypothesized to reflect periods of increased or decreased organization around the cognitive construct of workload. These segments were identified by a moving average smoothing of the Shannon entropy over the length of the performance and then related to team speech, actions and team responses to endogeneous and exogeneous task changes. Two-person teams in an unscripted map naviga-tion task developed a common, dominant coordination dynamic for workload whose rhythm was disrupted by exogeneous changes to the task. The entropy fluctuations during these disruptions differed in magnitude and duration within and across performances and were associated with qualitative and quantitative changes in team organization. Similar results were obtained with three and six person teams on other complex tasks. These results indicate that neurodynamic measures may be reliable, sensitive and valid indicators of the changing neurodynamics of teams around which standardized quantitative models can be developed.

Mapping Neurophysiologic Synchrony Attractor States and Entropy Fluctuations during Submarine Piloting and Navigation

Our objective was to apply ideas from complexity theory to derive expanded models of Submarine Piloting and Navigation (SPAN) showing how teams cognitively respond to task changes and how this was altered with experience. The cognitive measure highlighted was an electroencephalography (EEG)-derived measure of engagement (EEG-E) that was modeled into a collective team variable termed neurophysiologic synchronies of engagement (NS_E) thus showing the engagement of each of 6 team members as well as the engagement of the team as a whole. We show that the dominant NS_E patterns were different for novice and experienced teams, and that experienced teams used a larger repertoire of potential NS_E patterns. Estimates of the Shannon entropy of the NS_E data streams provided a quantitative history of NS_E fluctuations which were associated with the efficiency of the SPAN teams in updating the ship’s position.

Workshop on Designing Computational Models of Collaborative Learning Interaction

During collaborative learning activities, factors such as students’ prior knowledge, motivation,roles, language, behavior and interaction dynamics interact with each other in unpredictable ways, making it very difficult to predict and measure learning effects. This may be one reason why the focus of collaborative learning research shifted in the nineties from studying group characteristics and products to studying group process. With an interest in having an impact on the group process in modern distance learning environments, the focus has recently shifted again – this time from studying group processes to identifying computational strategies that positively influence group learning. This shift toward mediating and supporting collaborative learners is fundamentally grounded in our understanding of the interaction described by our models of collaborative learning interaction. In this workshop, we will explore the advantages, implications, and support possibilities afforded by the various types of computational models of collaborative learning processes.