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Coordination and Collective Performance: Cooperative Goals Boost Interpersonal Synchrony and Task Outcomes

View Article: PubMed Central - PubMed

ABSTRACT

Whether it be a rugby team or a rescue crew, ensuring peak group performance is a primary goal during collective activities. In reality, however, groups often suffer from productivity losses that can lead to less than optimal outputs. Where researchers have focused on this problem, inefficiencies in the way team members coordinate their efforts has been identified as one potent source of productivity decrements. Here, we set out to explore whether performance on a simple object movement task is shaped by the spontaneous emergence of interpersonally coordinated behavior. Forty-six pairs of participants were instructed to either compete or cooperate in order to empty a container of approximately 100 small plastic balls as quickly and accurately as possible. Each trial was recorded to video and a frame-differencing approach was employed to estimate between-person coordination. The results revealed that cooperative pairs coordinated to a greater extent than their competitive counterparts. Furthermore, coordination, as well as movement regularity were positively related to accuracy, an effect that was most prominent when the task was structured such that opportunities to coordinate were restricted. These findings are discussed with regard to contemporary theories of coordination and collective performance.

No MeSH data available.


Illustration of the frame-differencing technique used to quantify movement. A full 60 s time-series of movement (i.e., pixel change) from a solo trial is shown in the top panel and a ‘zoomed’ 2 s (35 s – 37 s) period in the middle panel. The lower panels depict every 6th frame (≈ ¼ s) from this 2 s period. The letter on each frame denotes the corresponding data point on the ‘zoomed’ time-series. As can be seen, the oscillatory pattern of the time-series data corresponds to the participant’s actions. ‘Valleys’ (i.e., low amount of movement/pixel change) match either picking up a ball from the container (e.g., frame A) or depositing it in the tube (e.g., frame D), while ‘peaks’ (i.e., high amount of movement/pixel change) match periods of movement between container and tube (e.g., frame B).
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Figure 2: Illustration of the frame-differencing technique used to quantify movement. A full 60 s time-series of movement (i.e., pixel change) from a solo trial is shown in the top panel and a ‘zoomed’ 2 s (35 s – 37 s) period in the middle panel. The lower panels depict every 6th frame (≈ ¼ s) from this 2 s period. The letter on each frame denotes the corresponding data point on the ‘zoomed’ time-series. As can be seen, the oscillatory pattern of the time-series data corresponds to the participant’s actions. ‘Valleys’ (i.e., low amount of movement/pixel change) match either picking up a ball from the container (e.g., frame A) or depositing it in the tube (e.g., frame D), while ‘peaks’ (i.e., high amount of movement/pixel change) match periods of movement between container and tube (e.g., frame B).

Mentions: Prior to analysis, the first 5 s of each trial was truncated in order to remove the countdown period and to eliminate any initial transient movements. A frame-differencing approach was then employed using a custom-written MATLAB script to convert the remaining 60 s of each trial into movement time-series. Specifically, each frame was halved vertically (in order to separate each participant’s movements) and compared to the corresponding half of the previous frame in terms of pixel change (see Figure 2). This provided two time-series (one per participant) of movement data for each trial (one time-series for individual trials).


Coordination and Collective Performance: Cooperative Goals Boost Interpersonal Synchrony and Task Outcomes
Illustration of the frame-differencing technique used to quantify movement. A full 60 s time-series of movement (i.e., pixel change) from a solo trial is shown in the top panel and a ‘zoomed’ 2 s (35 s – 37 s) period in the middle panel. The lower panels depict every 6th frame (≈ ¼ s) from this 2 s period. The letter on each frame denotes the corresponding data point on the ‘zoomed’ time-series. As can be seen, the oscillatory pattern of the time-series data corresponds to the participant’s actions. ‘Valleys’ (i.e., low amount of movement/pixel change) match either picking up a ball from the container (e.g., frame A) or depositing it in the tube (e.g., frame D), while ‘peaks’ (i.e., high amount of movement/pixel change) match periods of movement between container and tube (e.g., frame B).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037181&req=5

Figure 2: Illustration of the frame-differencing technique used to quantify movement. A full 60 s time-series of movement (i.e., pixel change) from a solo trial is shown in the top panel and a ‘zoomed’ 2 s (35 s – 37 s) period in the middle panel. The lower panels depict every 6th frame (≈ ¼ s) from this 2 s period. The letter on each frame denotes the corresponding data point on the ‘zoomed’ time-series. As can be seen, the oscillatory pattern of the time-series data corresponds to the participant’s actions. ‘Valleys’ (i.e., low amount of movement/pixel change) match either picking up a ball from the container (e.g., frame A) or depositing it in the tube (e.g., frame D), while ‘peaks’ (i.e., high amount of movement/pixel change) match periods of movement between container and tube (e.g., frame B).
Mentions: Prior to analysis, the first 5 s of each trial was truncated in order to remove the countdown period and to eliminate any initial transient movements. A frame-differencing approach was then employed using a custom-written MATLAB script to convert the remaining 60 s of each trial into movement time-series. Specifically, each frame was halved vertically (in order to separate each participant’s movements) and compared to the corresponding half of the previous frame in terms of pixel change (see Figure 2). This provided two time-series (one per participant) of movement data for each trial (one time-series for individual trials).

View Article: PubMed Central - PubMed

ABSTRACT

Whether it be a rugby team or a rescue crew, ensuring peak group performance is a primary goal during collective activities. In reality, however, groups often suffer from productivity losses that can lead to less than optimal outputs. Where researchers have focused on this problem, inefficiencies in the way team members coordinate their efforts has been identified as one potent source of productivity decrements. Here, we set out to explore whether performance on a simple object movement task is shaped by the spontaneous emergence of interpersonally coordinated behavior. Forty-six pairs of participants were instructed to either compete or cooperate in order to empty a container of approximately 100 small plastic balls as quickly and accurately as possible. Each trial was recorded to video and a frame-differencing approach was employed to estimate between-person coordination. The results revealed that cooperative pairs coordinated to a greater extent than their competitive counterparts. Furthermore, coordination, as well as movement regularity were positively related to accuracy, an effect that was most prominent when the task was structured such that opportunities to coordinate were restricted. These findings are discussed with regard to contemporary theories of coordination and collective performance.

No MeSH data available.