Tracing neurodynamic information flows in healthcare teams during simulation training
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1
University of California, Los Angeles, United States
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2
Jump Simulation and Education Center, United States
The ubiquity of teams in society has made the development of dynamic models of teamwork a priority for many sectors of the human factors community, especially models that can be applied in multiple team settings through the capture of generic properties of team member dynamics. 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 goal is to describe how meaning can be extracted from large-scale dynamical data to make inferences about teamwork that are useful in both the theoretical and practical sense.
We have developed an information-organization approach for detecting and quantitating the fluctuating neurodynamic organizations in teams. Neurodynamic organization is the propensity of team members to enter into prolonged (seconds-minutes) metastable neurodynamic relationships as they encounter and resolve disturbances to their normal rhythms.
Team neurodynamic organizations were detected and modeled by transforming the physical units of each team member’s EEG power levels into Shannon entropy-derived information units about the team’s organization and synchronization. Entropy is a measure of the variability or uncertainty of information in a data stream. This physical unit to information unit transformation bridges micro level social coordination events with macro level expert observations of team behavior allowing multimodal comparisons across the neural, cognitive and behavioral time scales of teamwork.
Each second, neurodynamic symbols (NS) were created showing the EEG power spectral densities (PSD) at the 1-40 Hz frequency bins for each team member. These data streams contained a history of the team’s across-brain neurodynamic organizations, and the degree of organization was calculated from a moving average window of the Shannon entropy of task segments.
An example of the symbol dynamics and the corresponding entropy profile for a healthcare team performance is shown in Figure 1. The symbol expressions were not temporally uniform, but were punctuated by segments of restricted NS expression that persisted for 1 – 4 minutes. Qualitatively, the NS expressions varied with the training session segments suggesting a neurodynamic form of task specificity.
Fig. 1 here
Fig. 1. Expression of neurodynamic symbols for a healthcare performance. The EEG PSD were separated into high, medium and low categories compared with the performance average. Every second each person’s EEG power levels were classified into the lower, middle, or upper third of their performance average and assigned the symbols -1, 1, and 3. With three team members and three EEG power levels, the symbol space consisted of 27 symbols. In the above figure a symbol was added for each second of the performance. The lines indicate junctions between different training segments. The fluctuating trace is the entropy of the symbol stream that was calculated over a 60s moving window.
More recently the information in the neurodynamic data streams of teams engaged in naturalistic decision making (healthcare (n=24), submarine navigation (n=12) or high school collaborative problem-solving (n=11) 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.
Most of the team information consisted of the information contained in an individual’s neurodynamic data stream. The information in an individual’s data stream that was shared with another team member was highly variable being 1-60% of the total information in another person’s data stream. From the shared, individual, and team information it becomes possible to quantitatively describe the dynamics of each team member during the task, as well as the neurodynamic interactions between members of the team.
The innovation of this study is the potential it raises for developing globally applicable quantitative models of both individual and team dynamics that will allow comparisons to be made across teams, tasks and training protocols.
References
Stevens, R. H., & Galloway, T. (2015). Modeling the neurodynamic organizations and interactions of teams. Social Neuroscience 11, 123-139. doi: 10: 1080/17470919.2015.1056883.
Stevens, R. H., Galloway, T., Halpin, D., & Willemsen-Dunlap, A. (2016). Healthcare teams neurodyamically reorganize when resolving uncertainty. Entropy, 18: 427, doi: 10.3390/e18120427
Stevens, R. H., & Galloway, T. (2017). Are neurodynamic organizations a fundamental property of teamwork? Frontiers in Psychology, May, 2017. doi: 10.3389/fpsyg.2017.00644.
Keywords:
Teamwork,
entropy,
information,
EEG,
team neurodynamics
Conference:
2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.
Presentation Type:
Oral Presentation
Topic:
Neuroergonomics
Citation:
Stevens
R,
Galloway
T,
Halpin
D and
Willemsen-Dunlap
A
(2019). Tracing neurodynamic information flows in healthcare teams during simulation training.
Conference Abstract:
2nd International Neuroergonomics Conference.
doi: 10.3389/conf.fnhum.2018.227.00045
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Received:
05 Mar 2018;
Published Online:
27 Sep 2019.
*
Correspondence:
PhD. Ronald Stevens, University of California, Los Angeles, Los Angeles, United States, immexr@gmail.com