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Physio-behavioral Synchronicity as an Index of Processes Supporting Team Performance Adam Strang, Gregory J. Funke, Benjamin A. Knott and Joel S. Warm Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2011 55: 1447 DOI: 10.1177/1071181311551301 The online version of this article can be found at: http://pro.sagepub.com/content/55/1/1447

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PROCEEDINGS of the HUMAN FACTORS and ERGONOMICS SOCIETY 55th ANNUAL MEETING - 2011

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Physio-behavioral Synchronicity as an Index of Processes Supporting Team Performance Adam Strang Consortium Research Fellows Program, Alexandria, VA Gregory J. Funke, Benjamin A. Knott, Joel S. Warm Air Force Research Laboratory, Wright-Patterson AFB, OH Recent research suggests that synchronicity in physiological and behavioral responses during team tasks could provide an objective measure of processes underlying team performance. In this study, twenty dyads completed a series of trials in a variant of video game Tetris (Quadra). Task performance was divided between the participants such that they had to work interdependently to succeed. Team-paired cardiac interbeat intervals (IBIs) and postural sway (anterior-posterior head motion) were analyzed using Cross-Sample Entropy (CSEn) as indices of physiobehavioral synchronicity. Quantity of team verbal communication (number of words spoken) and a survey measure of team cohesion were also assessed. An increase in team performance was found to be associated with a decrease in IBI synchronicity, while an increase in team verbal communication was related to both an in increase postural sway synchronicity and team cohesion. Overall, this research supports the assertion that metrics of team synchronicity may serve as useful surrogate indices of team processes and performance.

Not subject to U.S. copyright restrictions DOI 10.1177/1071181311551301

INTRODUCTION Team researchers have frequently been interested in identifying the behavioral, perceptual, and cognitive processes that contribute to team performance. For example, team researchers have found that team communication is a moderate predictor of team performance and team cohesion (e.g., Barrick, Stewart, Neubert, & Mount, 1998). Similarly, research suggests that team cohesion is a moderate predictor of team performance and that this relationship is stronger for “real world” sport and military teams compared to those that might be composed artificially in laboratory experiments (e.g., Mullen & Copper, 1994; Oliver, Hoover, Hayes, & Pandhi, 1999). Recent research by several investigators (e.g., Henning, Boucsein, & Gil, 2001; Shockley, Baker, Richardson, & Fowler, 2007; Shockley, Santana, & Fowler, 2003) has attempted to examine physiological and behavioral factors that might underlie and/or contribute to team processes and performance. This is important because physiological and behavioral responses may provide additional insights into the development of team processes, and could also serve as objective and sensitive measures of team performance to complement existing subjective measures. For example, Henning et al. (2001) had two-person teams take part in a cooperative computer task where the goal was to move a square object through a 2-dimensional maze while sharing joint control over the square’s planar movements (updown and left-right). Their results indicated that greater team synchronicity, assessed by cross correlating teammates’ cardiac interbeat intervals (IBIs), was associated with faster maze completion times. Henning and colleagues suggested that the observed cardiac synchronicity may be caused by coordinated movement between team members and could be used in the future as a metric to predict team performance in cooperative tasks. Correspondingly, Shockley and colleagues (2003) asked two-person teams to engage in a visual puzzle task while anterior-posterior postural sway was recorded from participants using magnetic hip markers. Shockley et al. found

that when teams worked cooperatively on the task their sway became synchronized, but that synchronicity did not occur when participants performed the same task independently in the presence of another individual (i.e., the mere presence of another person was not sufficient to cause synchronicity). Interestingly, this effect was observed regardless of whether or not the participants faced one another during the task. Shockley termed this phenomenon postural entrainment and posited that it might occur because of constraints placed on postural sway by the dynamics of the verbal conversation (e.g., talking, gesturing, listening, and visual inspection) required to complete the task. In a follow-up experiment, Shockley, Baker, Richardson, and Fowler (2007) similarly found that convergent speaking patterns do moderate postural entrainment in two-person teams, but that the effect is defined by each team’s own unique conversation dynamics. Overall, these results suggest that behavioral and physiological phenomenon, such as postural entrainment and cardiac rhythm synchronicity, may be useful indices of team processes and lend insight into team performance. One novel aspect of the research conducted by Shockley and colleagues (2003, 2007) was the use of a relatively new analysis for measuring synchronicity between two time-series known as cross-recurrence quantification analysis (CRQA). The advantage of CRQA is that it is robust against “noise” intrinsic to many physiological and behavioral responses (Webber & Zbilut, 2005). However, as an alternative to CRQA, Cross-Sample Entropy (CSEn) offers a further advantage for analyzing synchronicity. Namely, it is relatively easy to interpret – high CSEn values (i.e., values farther away from zero) indicate less synchronicity, while low values (i.e., values near zero) indicate greater synchronicity, while still providing similar robustness against inherent noise in physiological and behavioral responses (Richman & Moorman, 2000). For those reasons, CSEn was adopted for analysis in the current experiment. To date, researchers have not yet attempted to examine the collective relationships between team performance, physio-behavioral synchronicity, perceived team cohesion,



and team communication. The purpose of the current study was to expand on previous research by examining those relations. Based on the findings of the previously reported experiments, it was hypothesized that team performance and physio-behavioral synchronicity (indexed by IBI and postural sway CSEn values) would be positively related, i.e., an increase in physio-behavioral synchronicity would be related to an increase in team performance. The same trend was posited for the relationship between physio-behavioral synchronicity and team verbal communication, indicated by the number of words spoken by the team. It was also hypothesized that team cohesion would be positively correlated with team communication. No specific hypothesis was made regarding the relationship between physiobehavioral synchronicity and team cohesion.

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Next Tetrominoe The Well Falling Tetrominoe Previously Placed Tetrominoes

METHODS Participants Forty participants (20 men and 20 women) between the ages of 18 and 31 (M = 23.53, SD = 3.30) completed this study. Participants were paired into twenty teams composed of same-sex dyads. Researchers ensured that team members did not know one another prior to the experiment. All participants reported normal, or corrected to normal, vision and hearing; no abnormal heart conditions (heart murmurs, pace makers, etc.); were not taking prescribed drugs that may compromise normal circulatory function (blood pressure medication, blood thinning medication, etc.); and had not had any caffeine 12 hours prior to the study. Though specific experience with the experimental task (Quadra) was not assessed, men reported having more consistent experience with video-games than women on a pre-test questionnaire (don’t play video games at all: men = 3, women = 11; one to five hours of game-play per week: men = 10, women = 9; six hours or more of game-play per week: men = 7, women = 0). Experimental Design This experiment employed a correlational design that included measures of Quadra score, IBI CSEn, sway CSEn, team words spoken, and team cohesion. Each team completed 3 Quadra trials during the experiment. Apparatus Team task. A Tetris variant (Quadra) was adapted for use in this experiment (Figure 1). In Quadra, a random sequence of tetrominoes fall down a playing field (the “well”). Participants’ were tasked to manipulate the falling tetrominoes (by moving them sideways and/or by rotating them in 90 degree steps) to create horizontal lines of blocks without gaps. Points were awarded upon completion of each line, and bonuses were awarded depending on the number and complexity of lines completed. In the current experiment, control of the tetrominoes was divided between team members; one participant controlled tetrominoe orientation (“rotation”), while the other controlled lateral tetrominoe placement in the well (“location”). This

Figure 1. Annotated screen-shot of Quadra game-play.

division of responsibilities required strategic cooperation between teammates to accomplish task goals (i.e., achieving high game scores). During the experiment, the speed of falling tetrominoes remained constant within and between trials, ensuring a relatively constant level of task difficulty across trials. In addition, if the well “filled” (i.e., participants could not place additional tetrominoes within the well) during a trial, Quadra cleared the well, thereby allowing participants to continue the trial without impacting their trial score. Electrocardiogram (ECG) Measurement. Two disposable electrodes were placed on the sternum (over the maxilla and xiphoid process) of each participant to record ECG at 480 Hz using two wireless radio transmitters (Cleveland Medical Devices Inc, BioRadio, model BR150-900) interfaced with custom recording software developed by the Air Force Research Laboratory (AFRL). Postural Sway Measurement. Anterior-posterior (AP) head motion for each participants was recorded using optical tracking systems (Seeing Machines Inc., faceLAB eye trackers) sampled at 30 Hz. Voice Recordings. Team oral communications were recorded using radio microphones (Sennheiser HMD 224 microphones) attached to worn headsets that recorded .wav files using Wavesurfer (v1.8.5) software. Team Cohesion. Perceived team cohesion was assessed using a version of the scale developed by Rozzell and Gundersen (2003) that was modified to apply to dyads. The questionnaire is a 14-item, 7-point Likert-style scale, with verbal anchors from “strongly disagree (1)” to “strongly agree (7).” Cohesion scores for this measure are obtained by summing user ratings across scale items. Scores on the scale can range from 14-98, with higher scores indicating greater perceived cohesion between teammates.



Procedure Participants completed three 20-minute trials during the experiment. During these trials participants faced one another while Quadra was displayed simultaneously on two Samsung SyncMaster 940BX 19-inch LCD monitors (model LS19HAXBB2/HAA) placed just below eye height, located directly between the two participants (Figure 2). Participants played the Quadra game using two Logitech Rumblepad 2 (model PN 940-000059) hand-held video-game controllers. “Rotator”

“Locator”

Figure 2. Experimental setup depicting two-person team with independent (but identical) Quadra displays. Participants interacted with the Quadra game using separate hand-held video-game controllers while equipped with microphone headsets to record their communications.

Before beginning the experiment, participants received written and verbal instructions describing the Quadra game. Participants were then assigned to a team role (either “rotation” or “location”); role assignment did not change between trials. Due to the relatively simplistic nature of the Quadra game and a desire to observe the development of physio-behavioral synchronicity between teammates, participants were not provided a practice trial before the experiment began. Instead, participants were expected to learn the task and develop performance strategies across the three experimental trials. During the trials, participants were free to communicate with each other verbally. After completing the final trial, both team members completed the team cohesion questionnaire. Scores on the questionnaire were collapsed across teammates prior to inferential analysis to provide an indicator of “average” perceived team cohesion. Data Analysis IBI synchronicity. A custom AFRL analysis program was used to identify sequential R-peaks in recorded ECGs. Interbeat intervals were calculated as the time (in milliseconds) between successive R-peaks. Each series of recorded IBIs was averaged within discrete and adjacent 3second epochs to achieve time-series of equal length (400 data points). After normalizing each time-series to unit variance, CSEn values (assessed in arbitrary units; a.u.) were estimated for team-paired IBIs in each trial using the algorithm

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developed by Richman and Moorman (2000). CSEn parameters (M = 2, vector length for comparisons; and r = 0.2, tolerance value) were selected based on previous research (Pincus, 1995). Based on these parameters, IBI CSEn values were restricted to a range of approximately 0.22, indicating complete synchronicity, to 2.04, indicating complete independence (i.e., total asynchrony). These bounds were determined by obtaining CSEn value for paired sine waves (deterministic systems with a completely synchronous relationship) and white noise (stochastic systems with a completely asynchronous relationship). The length of these time-series was equal to those of the observed IBI time-series. Postural sway synchronicity. Head motion data were passed through a 2nd order Butterworth low-pass filter with a 12 Hz cutoff. The time-series were then incremented, i.e., differenced, according to xi+1 – xi, where x is anteriorposterior (AP) head position and i is the ith sampled data point in each time-series. Because the sampling rate of head position was fixed, incrementing is equivalent to taking the first derivative of head position, i.e., head velocity. This procedure was done to reduce long-range drift in the head position data (Ramdani, Seigle, Lagarde, Bouchara, & Bernard, 2009). The incremented time-series were then normalized to unit variance, team-paired, and CSEn values (M = 4, r = 0.1) were calculated for discrete two-minute windows (3600 data points) in each trial. The selected CSEn parameters were different from those used to calculated IBI CSEn to prevent flooring effects. This resulted in ten CSEn values for each team from which the mean was calculated as an indicator of “average” postural sway synchronicity for each trial. The bounds for sway CSEn values were approximately 0.31-2.44, and were determined using the same procedure described above. Team communication. A custom word-stress analysis software packaged developed by AFRL was used to detect the number of words spoken by each participant during each trial. This was done by detecting gender-specific fundamental frequencies and amplitude changes that are unique to spoken words. The number of detected words was then summed across teammates for each team and trial. RESULTS As a manipulation check, team Quadra scores, IBI CSEn, sway CSEn, and the number of words spoken by each team were examined using separate 2 × 3 mixed factorial analyses of variance (ANOVAs) to determine the potential effects of gender and trial on those factors. In these analyses, the Greenhouse-Geisser correction was employed to correct for violations of the sphericity assumption. Results showed no effect of gender or gender × practice trial interaction for any measure (all p > .05). However, Quadra scores increased across trials, F (1.86, 33.38) = 13.18, p < .05 (Figure 3). Follow-up paired-sample t-tests revealed a significant increase in Quadra scores from trial 2 to trial 3, t (19) = 3.53, p < .05, but not from trial 1 to 2 (p > .05). Based on the above analyses, it was determined to be reasonable to collapse dependent measures across trials, and to disregard gender in subsequent analyses. From the collapsed data, Pearson Product-moment Correlations (r) were



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Figure 3. Mean Quadra Score across trials (T1, T2, and T3). Star (*) denotes a significant pair-wise difference. Error bars are standard errors. Table 1. Intercorrelations of the dependent measures. IBI CSEn

— .45* -.13 -.03 .22

— -.01 .13 -.06

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— -.54* -.05

Words Spoken

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Team Cohesion

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The purpose of the current experiment was to expand on previous research by examining the relationships between team performance, physio-behavioral synchronicity, perceived team cohesion, and team verbal communication. It was hypothesized that team performance and physio-behavioral synchronicity would be positively correlated. The results of this experiment were opposite to expectations, since IBI synchronicity was shown to be negatively related to task performance, while postural sway synchronicity showed no relationship to performance. Consistent with expectations and previous research, team cohesion was positively correlated with team communication. Though no specific hypothesis was made regarding the relationship between physio-behavioral synchronicity and team cohesion, the results of the current

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r = .481

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calculated between each pair of dependent measures (Table 1). These analyses revealed significant positive correlations between Quadra score and IBI CSEn, and between the number of words spoken by the team and team cohesion, and a significant negative correlation between number of words spoken and sway CSEn (all p < .05). The positive Quadra score-IBI CSEn relationship indicated that an increase in Quadra score was related to a decrease in IBI synchronicity (Figure 4a; recall that an increase in CSEn indicates a decrease in synchronicity). However, team number of words spoken was related to both an increase in perceived team cohesion (Figure 4b) and an increase in postural sway synchronicity (Figure 4c).

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Figure 4. Scatter plots depicting (a) Quadra score-IBI CSEn, (b) words spoken-team cohesion, and (c) words spoken-sway CSEn relationships.

experiment indicate that cohesion and synchronicity are not directly related. Regarding the finding that a decrease IBI synchronicity was related to improved Quadra performance, two possible explanations suggest themselves. First, previous experiments examining postural sway and cardiac rhythm synchronicity have focused on teammates performing similar task roles (i.e., Henning et al., 2001; Shockley et al., 2003; Shockley et al., 2007). Participants in the current experiment had a similar and cooperative goal (achieving high Quadra scores), but differentiated and independent task roles (i.e., rotator and locator). It may be that in team tasks characterized by differentiated team roles, successful team strategies will result in less synchronicity between teammates. It should be noted, though, that team members still exhibited a significant degree of synchronicity as indicated by the fact that the observed



team IBI CSEn value all fell below the upper bound (i.e., they do not reflect a completely asynchronous relationship). Second, less synchronicity may be an indicant of healthy team processes supporting task performance. For example, moderate variability within the temporal dynamics of biological systems, including postural motion, has been posited to be an indicator of healthy behavior (Strang & DiDomenico, 2010; Pellecchia, Shockley, & Turvey, 2005). Specifically, researchers have theorized that temporal variability might reflect flexibility and the potential for adaptability in neuromuscular and physiological systems (James, 2005; Lipsitz & Goldberger, 1992). In the current experiment, a certain degree of asynchrony may signify greater team plasticity in task performance, ultimately resulting in higher team scores. Thus, the identified relationship of reduced physio-behavioral synchrony with improved team performance might reflect team flexibility in meeting task demands. As a topic of future research, it would be interesting to assess the degree of team synchrony across a longer period of task performance and team development to better assess the course of its evolution. With regard to the relationship between team verbal communication and postural sway synchronicity, the results of the current experiment support the work of Shockley and colleagues (2003, 2007), in that team verbal communication increased postural entrainment, i.e., synchronicity. Interestingly, sway synchronicity was not significantly related to team performance in the current experiment, suggesting that IBI and sway synchronicity are influenced by separate team processes, such as team role or team strategy and team communication, respectively. This differentiability may prove quite useful to team researchers as each measure provides an independent “window” for understanding team processes and performance. The near zero correlations between measures of synchronicity and team cohesion observed in this experiment are also intriguing. On the one hand, it seems intuitive that they should be related, in that perceived team member similarity can contribute to feelings of cohesion. On the other hand, physio-behavioral synchronicity, particularly at the level of autonomic physical activity and nervous response, is likely to occur without conscious awareness, perhaps explaining the dissociation between them. In accord with previous research linking team communication and team cohesion (e.g., Barrick et al., 1998), increases in team verbal communication were related to increases in perceived team cohesion in this experiment. It has previously been posited that team processes, such as team communication, foster feelings of cohesion among team members (e.g., Barrick et al., 1998), and the results of the current experiment seem to support that viewpoint. In sum, the results of this experiment suggest that metrics of team synchronicity may be useful indicators of team processes and team performance. In addition, these findings further support the utility of recently-developed nonlinear methods for assessing physio-behavioral data. Topics for future research stemming from this experiment include evaluating available nonlinear methods to determine which are most appropriate and sensitive for analyzing synchronicity

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data, and further research exploring the development of physiological and behavioral synchronicity in teams, as discussed previously. REFERENCES Barrick, M.R., Stewart, G.L., Neubert, M.J., & Mount, M.K. (1998). Relating member ability and personality to work-team processes and team effectiveness. Journal of Applied Psychology, 83, 377-391. Henning, R.A., Boucsein, W., & Gil, M.C. (2001). Social-physiological compliance as a determinant of team performance. International Journal of Psychophysiology, 40, 221-232. 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. Mullen, B., & Copper, C. (1994). The relation between group cohesiveness and performance: An integration. Psychological Bulletin, 115, 210-227. Oliver, L.W., Harman, J., Hoover, E., Hayes, S.M., & Pandhi, N.A. (1999). A quantitative integration of the military cohesion literature. Military Psychology, 11, 57-83. Pellecchia, G., Shockley, K., & Turvey, M.T. (2005). Concurrent cognitive task modulates coordination dynamics. Cognitive Science, 29, 531-55. Pincus, S. (1995). Approximate entropy (apen) as a complexity measure. Chaos, 5, 110-117. Ramdani, S., Seigle, B., Lagarde, J., Bouchara, F., & Bernard, P.L. (2009). On the use of sample entropy to analyze human postural sway data. Medical Engineering and Physics, 31, 1023-1031. 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. Rozell, E.J., & Gundersen, D.E. (2003). The effects of leader impression management on group perceptions of cohesion, consensus, and communication. Small Groups Research, 34, 197-222. Shockley, K., Baker, A.A., Richardson, M.J., & Fowler, C.A. (2007). Articulatory constraints on interpersonal postural coordination. Journal of Experimental Psychology: Human Perception and Performance, 33, 201-208. Shockley, K., Santana, M.V., & Fowler, C.A. (2003). Mutual interpersonal postural constraints are involved in cooperative conversation. Journal of Experimental Psychology: Human Perception and Performance, 29, 326-332. Strang, A.J., & DiDomenico, A.T. (2010). Postural control: Age-related changes in working-age men. Professional Safety, 55, 27-32. Webber, C.L., & Zbilut, J.P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. In M.A. Riley & G.C. Van Orden (Eds.). Tutorials in contemporary nonlinear methods for the behavioral sciences. Retrieved from http://www.nsf.gov/sbe/bcs/pac/nmbs/ chap3.pdf.
