My current research involves evaluating and modeling team states and dynamics using noninvasively (i.e., minimally disruptive) collected physiological and behavioral data. Since measuring team states and dynamics often requires evaluating multivariate longitudinally collected data, I am currently investigating methods that can effectively accomplish this. Specifically, I am interested in combining higher order network analyses that can deal with multivariate data streams (e.g. hypergraphs or multiplex networks) with analyses designed to evaluate structure in multidimensional data (e.g., tensor analysis) in order to understand and model team states, team dynamics, and team growth over time. This project will involve using these techniques to examine multivariate datasets collected from teams in order to understand and model team states, team dynamics, and team growth over time.

References:

Tolston, M. T., Funke, G. J., Riley, M. A., Mancuso, V., & Finomore, V. (2020). Using conceptual recurrence analysis to decompose team conversations. In McNeese, M., Salas, E., & Endsley, M. (Ed.), Contemporary REsearch: Models Methodologies, and Measures in Distributed Team Cognition (1st ed., pp. 235-256). CRC Press.

Tolston, M. T., Funke, G. J. & Shockley, K. (2020). Comparison of cross-correlation and joint-recurrence quantification analysis based methods for estimating coupling strength in non-linear systems. Frontiers in Applied Mathematics and Statistics 6(1): doi: 10.3389/fams.2020.00001

Tolston, M. T., Riley, M. A., Mancuso, V., Finomore, V., Funke, G. J. (2019). Beyond frequency counts: Evaluation of conceptual recurrence analysis metrics to index semantic coordination in team communications. Behavior Research Methods 51(1): 342-360.

key words

Teams; Human Machine Teaming; Dynamical Systems; Complex Systems; Interpersonal Coordination; Communication; Complex Networks;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants