Temporal Complexity in Team Coordination ... - SAGE Journals

PROCEEDINGS of the HUMAN FACTORS and ERGONOMICS SOCIETY 57th ANNUAL MEETING - 2013

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Temporal Complexity in Team Coordination Associated with Increased Performance in a Fast-Paced Puzzle Task Adam J. Strang Oak Ridge Institute for Science and Education, Oak Ridge, TN Samantha Epling Ball Aerospace and Technologies Corporation, Fairborn, OH Gregory J. Funke & Sheldon M. Russell United States Air Force Research Laboratory, Wright-Patterson AFB, OH 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.

Not subject to U.S. copyright restrictions. DOI 10.1177/1541931213571274

INTRODUCTION Efficient and effective coordination is essential for team task performance and serves as an index of team perceptual and cognitive processes (e.g., strategy, workload; Gorman, Amazeen, & Cooke, 2010; Salas, Bowers, & Cannon-Bowers, 1995; Salas & Fiore, 2004). Previous studies examining team coordination through observations of team behavior, including activities such as joint response times (e.g., Vesper, van der Wel, Knoblich, & Sebanz, 2011) and recorded verbal communication (e.g., Volpe, Cannon-Bowers, Salas, & Spector, 1996), have mostly relied on descriptive summary statistics (e.g., frequency, mean, and standard deviation). These measures index a number of important coordination dynamics (e.g., the overall amount, central tendency, and relative variability of a team behavior), but do not characterize temporal structure (e.g., repeating patterns) that might exist in team coordination as it occurs over time. Nonlinear time-series measures are specifically designed to quantify temporal structure in timedependent phenomenon like human heart rhythm (Richman & Moorman, 2000), financial trends (Pincus & Kalman, 2004), and machine vibration (Yan & Gao, 2007). Entropy statistics (e.g., Sample

Entropy and its predecessor, Approximate Entropy), represent a family of nonlinear measures that have proven particularly popular for describing the degree of temporal complexity (or predictability) in a time series – where low entropy values reflect a system exhibiting low complexity (i.e., a predictable temporal pattern) and high values indicate a system exhibiting high complexity (i.e., a less predictable pattern; Pincus, 1991). The popularity of entropy statistics comes from their ability to render accurate and reliable estimates with short time series (N > 50 in some cases) and their close agreement with theoretical predictions of complexity in modeled systems with known temporal dynamics (Pincus, 1995). In a recent experiment, Strang et al. (2012) used Sample Entropy (SEn; Richman & Moorman, 2000) to examine temporal structure in categorically coded team communications made by teams performing a simulated air defense task. The main findings from that study were: a) confirmation that meaningful temporal structure existed in team communication (i.e., communication patterns were not random, but instead contained semideterministic temporal structure), and b) SEn values were influenced by experimental manipulation – communication exhibited lower complexity (decreased SEn) in teams exposed to high task


difficulty. Strang and colleagues (2012) argued that their results confirmed the viability of SEn for assessing temporal structure in team communication, a conclusion that aligns with those of other recent studies exploring the utility of nonlinear measures for understanding team communication (Gorman, Amazeen, & Cooke, 2010; Gorman, Cooke, Amazeen, & Fouse, 2012; Russell et al., 2012). However, there are a number of other team behaviors, such as coordinated motor responses, that might also exhibit meaningful temporal structure, as well as share a more direct link to team performance (as compared to a supporting behavior like communication). If so, examining the temporal structure in team motor responses using a measure like SEn could enable a more holistic description of team coordination, which is potentially useful for advancing basic theory on this topic, and also provide additional means to more closely examine and characterize the team coordination-performance relationship. Given this, the goals of the current study were to: a) determine if meaningful temporal structures existed (using SEn) in the continuous motor responses (gamepad button presses) of dyadic teams performing a cooperative motor task (Quadra – a variant of videogame Tetris), and b) explore the relationships between team performance outcomes, traditional coordination measures, and SEn. METHODS Participants Forty participants (20 men, 20 women), recruited from local universities and the greater Dayton, OH, area, completed this experiment (Mage = 24.48, SD = 2.84). Participants were assigned to same-sex dyadic teams. Care was taken to ensure that teammates had no familiarity with one another prior to the experiment. Though specific experience with the Quadra task was not assessed, men reported having more consistent experience with video-games than women on a pre-test questionnaire. Procedures were approved by an Institutional Review Board, and participants provided informed consent prior to the experiment. Experimental Task: Quadra In Quadra (Fig. 1), game pieces fall down a playing well in random order. As they fall,

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participants attempt to complete horizontal rows (without gaps) by translating and rotating pieces; this is achieved by manually pressing buttons located on a handheld gamepad. Points are earned when a line is completed with bonuses given for completing multiple lines simultaneously. In the current study, the type of motor response (i.e., rotation or location) was divided across teammates such that one participant controlled piece translation while the other controlled rotation. During game play, researchers recorded the sequential type (translation or rotation) and time (time-interval, in milliseconds, between responses) of motor responses. Gameplay difficulty was held relatively constant across teams and trials by keeping the speed of falling game pieces uniform. Finally, if the well was filled during a trial it was immediately cleared without penalty to enable continuous gameplay.

Figure 1. Screen-shot of the Quadra game-play environment.

Procedure Prior to the experiment, participants were given written and verbal instructions describing the task and their performance goal, which was to obtain the highest score possible. Following this, dyads completed three 20-minute trials of the experimental task – two practice trials and a performance trial. Only data obtained from the performance trial are examined here. For all trials, dyads were permitted to talk (in order to discuss strategy and coordinate action) and stood facing one another while the game was displayed on back-to-back computer monitors set just below eye level. Participants were provided with individual gamepads with which to make their responses.


Data Analysis The type and time of motor responses were arranged into time-series (Fig. 2). a)

Motor Respnse Type

2 Rotation

Translation1 1

b)

51

101 151 Button Presses

201

6000

Time Interval (ms)

5000 4000

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series windows, from which the mean was calculated to indicate the central tendency of SEn in each trial. SEn parameters M and r were set at 2 and 0, respectively, for type time series following the recommendations and procedures described in Strang et al. (2012) for examining nominal data. Coordination measures for time responses were variability (standard deviation; SDtime) and SEn (SEntime). In this context, SDtime represented the relative dispersion (or range) in response timing over a designated time interval. SEn parameters M and r were set at 3 and .1, respectively, for time data, following a parameter optimization procedure described by Ramdani et al. (2009) for examining interval-scale data. Finally, all time metrics were estimated using the same timeseries windowing technique applied to SEntype.

3000

RESULTS & DISCUSSION 2000 1000 0 1

51

101 151 Button Presses

201

Figure 2. Cropped type (a) and time (b) time series observed from one dyadic team.

Coordination measures observed from type responses were translation-to-rotation ratio (T/Rratio) and Sample Entropy (SEntype). T/Rratio represented the total number of translation versus rotation responses in each trial. This metric is akin to the anticipation ratio (e.g., ratio of requests versus volunteered information) used in communication studies to examine the “flow” of team information exchange (Entin & Entin, 2001). In this context, T/R ratio simply indicated the balance of motor response alternatives evoked in each trial. As described earlier, SEn is a nonlinear measure that quantifies the degree of temporal complexity in a time series. It is defined as the natural log of the probability that matches of vector lengths, M, also match for vector lengths, M + 1; given a tolerance, r, and without an allowance for self-matching (Richman & Moorman, 2000). Because entropy statistics are known to be sensitive to differences in time-series length (Pincus & Goldberger, 1994), SEn was estimated in consecutive and non-overlapping 800 point time-

Determination of meaningful temporal structure A critical assumption of nonlinear analysis is that the temporal structure of the system under examination is not stochastic (random), but instead contains some degree of determinism (i.e., a lawful pattern). The presence of determinism is established using surrogation tests, of which there are many forms. In this study we employed a form of surrogation test (for both type and time timeseries) that is popular, simple, and intuitive. The first step in performing these tests was to randomly shuffle the data points contained in the original type and time time-series. This effectively dismantled any deterministic temporal structure that may have originally existed in the data. (Note: Random shuffling only disrupts the temporal properties of a time-series, thus it exerts no influence on either the magnitude or relative frequency of responses. This means that SDtime and T/Rratio were unaffected by this manipulation.) SEn was then calculated for the randomlyshuffled time series (using the same parameters and procedures described previously). Finally, SEn obtained from the random surrogates was compared to estimates obtained from the original (dyadic team) time-series using paired t-tests. Results indicated that SEn values obtained from the original data were lower (i.e., exhibited less complex


patterning) for both type and time time-series, compared to values obtained from the random surrogates (Table 1). These results confirm that meaningful determinism existed in the temporal structure of both responses, thus permitting further examination of the degree to which structure is influenced by experimental factors (e.g., team performance differences). Table 1. SEn means and standard errors (in parentheses) of originally sampled time series and randomly-shuffled surrogate time series. Time-series

Original

Random Surrogate

t

SEntype

.644 (.003)

.678 (.003)

10.84*

SEntime

1.374 (.014)

1.414 (.012)

7.48*

Note. t-crit df = 19, α = .05 = 2.09. * p < .05

Relationships between Coordination Measures and Team Performance Pearson correlations (r) were used to examine the relationships between coordination metrics and performance (Quadra Score; Table 2). Only one metric, SEntime, exhibited a statistically significant relationship with team performance (Quadra Score). The direction of this relationship was positive, indicating that an increase in team performance was related to an increase in the temporal complexity of response timing (i.e., reduced deterministic patterning in response timing). In considering this effect, it should first be noted that simple detection of this relationship (regardless of its direction) supports the position that temporal structure is an important dynamic of coordination, in-so-far as accounting for team performance differences is concerned. Thus, a practical take-away from this study is that assessment of coordination temporal structure, using nonlinear measures, is likely important to consider when examining team coordinationperformance relationships. Second, moderate levels of temporal complexity in the behavior of biological systems, including human postural sway (Strang & DiDomenico, 2010), heart rhythm (Richman & Moorman, 2000) and hand tremor (Vaillancourt & Newell, 2000), has been linked to positive health. This has led to a general hypothesis that moderate levels of chaos within the temporal pattern of a

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biological system may indicate the positive attributes of flexibility and/or the potential for adaptability (James, 2005; Lipsitz & Goldberger, 1992). To our knowledge this is the first study to support a similar conclusion in the explicit examination of team motor coordination, though a similar finding and interpretation has been reported in research examining team communication (Gorman et al., 2012) Table 2. Correlations between Quadra scores and coordination measures for dyadic teams. Quadra Score 1.00

SEntype

SEntype

.24

1.00

SEntime

.54*

.43

1.00

SDtime

-.39

-.35

-.67*

1.00

T/Rratio

-.18

.28

.24

.06

Quadra Score

SEntime

SDtime

T/Rratio

1.00

Note. Dyad r-crit df = 18 = .44. * p < .05

Finally, a negative correlation was detected between SDtime and SEntime, indicating that an increase in response timing variability was related to a decrease in temporal complexity. Restated, as the relative dispersion (or range) in response timing increased, the temporal patterning of those responses became more deterministic. Interestingly, a similar relationship has been reported in human postural control studies, where participants with existing pathology (e.g., cerebral palsy; Donker, Ledebt, Roerdink, Savelsbergh, & Beek, 2008) and those exposed to experimental constraints (e.g., cognitive load; Donker, Roerdink, Greven, & Beek, 2007) have been shown to exhibit postural sway with higher dispersion, but less temporal complexity, as compared to healthy and unconstrained counterparts. There is debate as to whether these coordination differences reflect reduced or compensatory postural control. In the current context, however, it appears that interpretation of these corresponding features of coordination are clearer cut (i.e., they likely indicate worse performance), as higher complexity (significant result) and reduced variability (a relationship that was not significant but still


moderately related; r ~ -.4) in response timing were related to better performance (higher score). CONCLUSION The two main findings of this study were that meaningful temporal structure was detected in both the type and time of continuous team motor responses using SEn, and that SEntime was the only coordination measured to exhibit a significant relationship with team performance. Together, these findings add to the growing body of research supporting the viability, utility, and importance of including assessments of temporal structure (via application of nonlinear measures) for understanding team processes and performance. ACKNOWLEDGEMENT This work was generously supported by an Air Force Office of Scientific Research (AFOSR) grant (Program Manager: Dr. Jay Myung). REFERENCES Donker, S.F., Ledebt, A., Roerdink, M., 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, M., 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, S. (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 N. 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, S. (1995). Approximate entropy (apen) as a complexity measure. Chaos, 5, 110-117. 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. Pincus, S.M. & Kalman, R. (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. Ramdani, S., Seigle, B., Lagarde, J., Bouchara, F., & Bernard,

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