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.
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Notes
Alternative numbering schemes such as assigning values of 3, 2 and 1 to the partitions designated as upper, middle or lower third were also tested. These resulted in an R 2 of >0.95 when the entropy of the NS_E data stream was compared with that generated from models where the vectors were assigned values of 3, 1 and −1. The resulting histogram displays were more difficult to understand and so the 3, 1 and −1 convention was retained.
Preliminary models were created with 400, 100, 25 and 16 nodes and 25 nodes provided the best balance between sensitivity and speed.
Across the 6 members of one team the median decontamination period was 840 milliseconds and within this window the first 160 ms and the last 320 ms were reference segments used for interpolation. The decontamination algorithm then examines each remaining data points (∼360 ms) and if 2 SD above the mean reference segment then they were interpolated. This resulted in ∼25 % of the segments being interpolated or ∼90 ms per second, or 45 ms/eye blink
References
Appleton J, Christenson S, Kim D et al (2006) Measuring cognitive and psychological engagement: validation of the student engagement instrument. J Soc Psychol 44:427–445
Berka C, Levendowski DJ, Cvetinovic MM, Petrovic MM, Davis G et al (2004) Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. Int J Hum-Comput Interact 17(2):151–170
Cooke NJ, Gorman JC, Kiekel PA (2008) Communication as team-level cognitive process. In: Letsky MP, Warner NW, Smith CAP (eds) Macrocognition in teams: theories and methodologies. Ashgate, Farnham, pp 51–64
Cooke NJ, Gorman JC, Rowe LJ (2009) An ecological perspective on team cognition. In: Salas E, Goodwin J, Burke CS (eds) Team effectiveness in complex organizations: cross-disciplinary perspectives and approaches. SIOP organizational frontiers series. Taylor & Francis, London, pp 157–182
Gorman JC, Cooke NJ, Amazeen PG (2010) Training adaptive teams. Hum Factors 52:295–307
Howard B (1996) Cognitive engagement in cooperative learning. In: Annual meeting of the eastern educational research association, Boston, MA, 21–24 February 1996. http://www.eric.ed.gov/ERICWebPortal/search/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED404352&ERICExtSearch_SearchType_0=no&accno=ED404352. Accessed 8 June 2011
Huber L (2008) Measuring cognitive engagement in middle school students. University of South Dakota thesis
Krieger AC, Ayappa I (2004) Comparison of the maintenance of wakefulness test (MWT) to a modified behavioral test (OSLER) in the evaluation of daytime sleepiness. J Sleep Res 13(4):407–411
Levendowski DJ, Berka C, Olmstead RE, Konstantinovic ZR, Davis G, Lumicao MN, Westbrook P (2001) Electroencephalographic indices predict future vulnerability to fatigue induced by sleep deprivation. Sleep 24 (Abstract Supplement): A243–A244
Lindenberger U, Li S-C, Gruber W, Muller V (2009) Brains swinging in concert: cortical phase synchronization while playing guitar. BMC Neurosci 10:22–34
Nowak A, Vallacher R (2007) Dynamical social psychology. Guildord Press, New York
Plummer J, Cook B, Diforio D, Schacter B, Sokolyanskaya I, Korde T (2007) Measuring cognitive engagement, vol. II. http://www.classmatandread.net/class/2.Measures~%20of~%20Engagement.pdf. Accessed 8 June 2011
Roberts E, Young RM (2008) Maintaining cognitive engagement in training scenarios using explicit cognitive models. In: Interservice/Industry service training and education conference (I/ITSEC), paper 8217, pp 1–9
Shannon CE (1951) Prediction and entropy of printed English. Bell Syst Tech J 30:50–64
Stevens RH, Galloway T, Berka C (2007) Allocation of time, workload, engagement and distraction as students acquire problem solving skills. In: Schmorrow D, Nicholson D, Drexler J, Reeves L (eds) Foundations of augmented cognition, 4th edn, pp 128–137
Stevens RH, Galloway T, Berka C, Sprang M (2009) Can neurophysiologic synchronies be detected during collaborative teamwork? In: Proceedings: HCI international 2009, San Diego, CA, 19–24 July 2009, pp 271–275
Stevens RH, Galloway T, Berka C, Behneman A (2010a) A neurophysiologic approach for studying team cognition. In: Interservice/Industry training simulation and education conference (I/ITSEC) 2010, Paper No 10135
Stevens RH, Galloway T, Berka C, Behneman A (2010b) Identification and application of neurophysiologic synchronies for studying team behavior. In: Proceedings of the 19th conference on behavior representation in modeling and simulation, pp 21–28
Stevens RH, Gorman G (2011) Mapping cognitive attractors onto the dynamic landscapes of teamwork. In: Proceedings of the 14th international conference on human-computer interaction, Orlando, FL, 9–14 July 2011
Stevens RH, Galloway T, Wang P, Berka C (2012) Cognitive neurophysiologic synchronies: what can they contribute to the study of teamwork? Hum Factors 54:489–502. 0018720811427296, first published on 12, 2011. doi:10.1177/0018720811427296
Warner N, Letsky M, Cowen M (2005) Cognitive model of team collaboration: macro-cognitive focus. In: Proceedings of the 49th human factors and ergonomics society annual meeting, Orlando, FL, 26–30 September 2005
Acknowledgements
We extend a special thanks to Adrienne Behneman for the data collection and to Drs. Jamie Gorman and Polemnia Amazeen for helpful discussions.
Approved for Public Release, Distribution Unlimited. “The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.” This is in accordance with DoDI 5230.29, January 8, 2009. This work was supported by NSF SBIR awards 1215327, 0822020, Office of Naval Research award N00014-11-M-0129, and an award from the Defense Advanced Research Projects Agency (DARPA) under contract numbers NBCHC070101, NBCHC090054 and W31P4Q12C0166.
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Stevens, R., Galloway, T., Wang, P. et al. Modeling the neurodynamic complexity of submarine navigation teams. Comput Math Organ Theory 19, 346–369 (2013). https://doi.org/10.1007/s10588-012-9135-9
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DOI: https://doi.org/10.1007/s10588-012-9135-9