Modeling the neurodynamic complexity of submarine navigation teams

Abstract: Our objective was to apply ideas from complexity theory to derive neurophysiologic models of Submarine Piloting and Navigation showing how teams cognitively organize around changes in the task and how this organization is altered with experience. The cognitive metric highlighted was an electroencephalography (EEG)- derived measure of engagement (termed NS_E) which was modeled into a collective team variable showing the engagement of each of 6 team members as well as the engagement of the team as a whole. We show that during a navigation task the NS_E data stream contains historical information about the cognitive organization of the team and that this organization can be quantified by fluctuations in the Shannon entropy of the data stream.

The fluctuations in the NS_E entropy were complex, showing both rapid changes over a period of seconds and longer fluctuations that occurred over periods of minutes. The periods of low NS_E entropy represented moments when the team’s cognition had undergone significant re-organization, i.e. when fewer NS_E symbols were being expressed.

Decreases in NS_E entropy were associated with periods of poorer team performance as indicated by delays/omissions in the regular determination of the submarine’s position; parallel communication data suggested that these were also periods of increased stress.

Experienced submarine navigation teams performed better than Junior Officer teams, had higher overall levels of NS_E entropy and appeared more cognitively flexible as indicated by the use of a larger repertoire of available NS_E patterns.

The quantitative information in the NS_E entropy may provide a framework for designing future adaptive team training systems as it can be modeled and reported in near real time.

Stevens R., Galloway T., Wang P., Berka C., Tan V., Wohlgemuth T., Lamb J. & Buckles R. (2012). Modeling the neurodynamic complexity of submarine navigation team.  Computational and Mathematical Organization Theory, August 2012; DOI 10.1007/s10588-012-9135-9

2017-11-15T12:05:59-07:00