Temporal Complexity in Team Coordination Associated with Increased Performance in a Fast-Paced Puzzle Task

Abstract

Coordination is a critical component of team performance. Nonlinear time-series measures, such as Sample Entropy (SEn), provide a novel means to examine temporal structure in team coordination. The goal for this study was to apply SEn to the continuous motor responses (gamepad button presses) of dyadic teams who performed a fast-paced puzzle task (Quadra – a variant of videogame Tetris). Inferential analyses were used to: a) determine if meaningful (i.e., deterministic) temporal structure existed in team responses using SEn, and b) examine correlations between team performance and coordination metrics (including SEn). Results confirmed that meaningful temporal structure existed in the sequential type and time of team motor responses. In addition, SEn was the only coordination metric to exhibit a significant relationship with team performance outcomes. Together, these findings support the viability and salience of nonlinear measures such as SEn in assessment of team coordination.

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References

Donker

S.F.

Ledebt

Roerdink

Savelsbergh

G.J.P

Beek

P.J.

(2008). Children with cerebral palsy exhibit greater and more regular postural sway than typically developing children. Experimental Brain Research, 184, 363-370.

Donker

S.F.

Roerdink

Greven

A.J.

Beek

P.J.

(2007). Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control. Experimental Brain Research, 181, 1-11.

Entin

E.E.

Entin

E.B.

(2001). Measures for evaluations of team processes and performance in experiments and exercise. Proceedings of the 2001 Command and Control Research and Technology Symposium, Annapolis, MD.

Gorman

J.C.

Amazeen

P.G.

Cooke

N.J.

(2010). Team coordination dynamics. Nonlinear Dynamics, Psychology, and Life Sciences, 14, 265-289.

Gorman

J. C.

Cooke

N. J.

Amazeen

P. G.

Fouse

(2012). Measuring patterns in team interaction sequences using a discrete recurrence approach. Human Factors, 54, 503-517.

James

R.C.

(2005). Considerations of movement variability in biomechanics research. In Stergiou

(Ed.), Innovative analyses of human movement: Analytical tools for human movement research (pp. 29-62). Champaign, IL: Human Kinetics.

Lipsitz

L.A.

Goldberger

A.L.

(1992). Loss of ‘complexity’ and aging: Potential applications of fractals and chaos theory to senescence. Journal of the American Medical Association, 267, 1806-1809.

Pincus

S.M.

(1991). Approximate entropy as a measures of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301.

Pincus

(1995). Approximate entropy (apen) as a complexity measure. Chaos, 5, 110-117.

10.

Pincus

S.M.

Goldberger

A.L.

(1994). Physiological time-series analysis: what does regularity quantify? American Journal of Physiology: Heart and Circulatory Physiology, 35, H1643-H1656.

11.

Pincus

S.M.

Kalman

(2004). Irregularity, volatility, risk, and financial market time-series. Proceeding of the National Academy of Science of the United States of America, 101, 13709-13714.

12.

Ramdani

Seigle

Lagarde

Bouchara

Bernard

P.L.

(2009). On the use of sample entropy to analyze human postural sway data. Medical Engineering and Physics, 31, 1023-1031.

13.

Richman

J.S.

Moorman

J.R.

(2000) Physiological time \ series analysis using approximate entropy and sample entropy. American Journal of Physiology: Heart and Circulatory Physiology, 278, H2039-H2049.

14.

Salas

Bowers

C.A.

Cannon-Bowers

J.A.

(1995). Military team research: 10 years of progress. Military Psychology, 7, 55-75.

15.

Salas

Fiore

S.M.

(2004). Team cognition: Understanding the factors that drive process and performance. Washington, DC: American Psychological Association.

16.

Russell

S.M.

Strang

A.J.

Funke

G.J.

Knott

B.A.

(2012). Recurrence quantification analysis used to assess team communication in simulated air battle management. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56, 468-472.

17.

Strang

A.J.

DiDomenico

A.T.

(2010). Postural control: Age-related changes in working-age men. Professional Safety, 55, 27-32.

18.

Strang

A.J.

Horwood

Best

Funke

G.J.

Knott

B.A.

Russell

S.M.

(2012). Examining temporal regularity in categorical team communication using sample entropy. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56, 473-477.

19.

Vesper

van der Wel

Knoblich

Sebanz

(2011). Making oneself predictable: reduced temporal variability facilitates joint action coordination. Experimental Brain Research, 211, 517-530.

20.

Vaillancourt

D.E.

Newell

K.M.

(2000). The dynamics of resting and postural tremor in Parkinson’s disease. Clinical Neurophysiology, 111, 2046-2056.

21.

Volpe

C.E.

Cannon-Bowers

J.A.

Salas

Spector

P.E.

(1996). The impact of cross-training on team functioning: An empirical investigation. Human Factors, 38, 87-100.

22.

Yan

Gao

R.X.

(2007). Approximate entropy as a diagnostic for machine health monitoring. Mechanical Systems and Signal Processing, 21, 824-839.