Measuring Patterns in Team Interaction Sequences Using a Discrete Recurrence Approach

Gorman, Jamie C.; Cooke, Nancy J.; Amazeen, Polemnia G.; Fouse, Shannon

doi:10.1177/0018720811426140

Cited by 84 publications

(69 citation statements)

References 31 publications

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“…With reference to team role, it is plausible that information regarding who was speaking simply did not provide the necessary resolution to detect changes in communication induced by workload. In reference to this interpretation, it is important to note that the manipulation effects detected by Gorman (2011) were based on assessments of triads as opposed to the dyads examined here. It is possible that in Gorman's study, higher role differentiation may have caused team role to serve as a more viable surrogated for more detailed communication information (e.g., if one role was linked almost exclusively to a specific type of semantic communication), which ultimately led to a greater ability to differentiated temporal patterns in team communication from this coding scheme.…”

Section: Addressing Questions 3 Andmentioning

confidence: 94%

“…Neither ApEn nor SEn have been applied to team communications data, though other nonlinear measures have recently been applied to team communications data with some success (e.g., Gorman, Amazeen, & Cooke, 2010;Gorman, Cooke, Amazeen, & Fouse, 2011). Based on this, the goal of the current experiment was to develop a method for applying SEn to a set of categorically coded team communication data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Examining Temporal Regularity in Categorical Team Communication Using Sample Entropy

Strang

Horwood

Best

et al. 2012

Proceedings of the Human Factors and Ergonomics Society Annual

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Communication is inherent to team coordination and performance. Nonlinear time-series measures, such as Sample Entropy (SEn), provide the opportunity to examine the temporal structure of team communication. The aim for this experiment was to develop a method for applying SEn to a set of categorically coded and sequential team verbal communications recorded during a dyadic Air Battle Management (ABM) simulation. Results showed that deterministic temporal regularity was detected in team communication for three categorical coding schemes applied to the data -team role, semantic content, and combined. For data coded for semantic content, SEn was able to detect an increase in communication temporal regularity for teams exposed to high workload. Practical and theoretical implications are considered.

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Section: Addressing Questions 3 Andmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Examining Temporal Regularity in Categorical Team Communication Using Sample Entropy

Strang

Horwood

Best

et al. 2012

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Stevens et al (2016) monitored EEG signals in surgical teams and measured their neurodynamic entropy while simultaneously capturing their communication activity. Figure 10A shows one team’s discrete recurrence plot (discrete RP; Gorman et al, 2012a) of turn-taking during team communication. For a sequence x of length N , the discrete RP is an N × N symmetric matrix, where if the value of x (j) is identical to the value of x (i), then a dot (“recurrent point”) is plotted at x (i,j) in the RP.…”

Section: Team Dynamics Across Levels Of Analysismentioning

confidence: 99%

Understanding and Modeling Teams As Dynamical Systems

et al. 2017

Self Cite

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By its very nature, much of teamwork is distributed across, and not stored within, interdependent people working toward a common goal. In this light, we advocate a systems perspective on teamwork that is based on general coordination principles that are not limited to cognitive, motor, and physiological levels of explanation within the individual. In this article, we present a framework for understanding and modeling teams as dynamical systems and review our empirical findings on teams as dynamical systems. We proceed by (a) considering the question of why study teams as dynamical systems, (b) considering the meaning of dynamical systems concepts (attractors; perturbation; synchronization; fractals) in the context of teams, (c) describe empirical studies of team coordination dynamics at the perceptual-motor, cognitive-behavioral, and cognitive-neurophysiological levels of analysis, and (d) consider the theoretical and practical implications of this approach, including new kinds of explanations of human performance and real-time analysis and performance modeling. Throughout our discussion of the topics we consider how to describe teamwork using equations and/or modeling techniques that describe the dynamics. Finally, we consider what dynamical equations and models do and do not tell us about human performance in teams and suggest future research directions in this area.

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“…considered as the client) in a mutually acceptable research design led to select the perception of task-cohesion and shared understanding as the phenomena under study: Both reflect an experience of togetherness within the teams, while having usually been investigated in distinct studies (deChurch and MesmerMagnus 2010). Both also have been shown to be related to team performance, especially in highly interdependent teams as are basketball teams (Gully, devine, and Whitney 1995;Mesmer-Magnus and deChurch 2009). The need to link both phenomena in a multiframework perspective has been recently formulated in sport (Filho, Tenenbaum, and Yang 2015), so that novel empirical investigations of togetherness within teams are needed.…”

Section: Experiencing Togetherness Through Team Functioningmentioning

confidence: 99%

“…It is here assumed that higher scores of ATG-T and GI-T account for high cohesion within the team (Wolf et al 2015). It has been shown to contribute to performance in highly interdependent teams, especially when interpersonal communication and synchronisation of actions are needed to perform (see Gully, devine, and Whitney 1995 in industrial and organisational settings; see Pescosolido and Saavedra 2012 in sports settings).…”

Section: Temporal Aspects Of Task-cohesionmentioning

confidence: 99%

Fluctuations of the experience of togetherness within the team over time: task-cohesion and shared understanding throughout a sporting regular season

Bourbousson

Fortes-Bourbousson

2016

Ergonomics

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Based on a diagnosis action research design, the present study assessed the fluctuations of the team experience of togetherness. Reported experiences of 12 basketball team members playing in the under-18 years old national championship were studied during a four-month training and competitive period. Time series analysis (Auto-Regressive Integrated Moving Average procedures) served to describe temporal properties of the way in which the fluctuations of task-cohesion and shared understanding were step-by-step experienced over time, respectively. Correlations, running-correlations and cross-lagged correlations were used to describe the temporal links that governed the relationships between both phenomena. The results indicated that the task-cohesion dimensions differed mainly for shared understanding dynamics in that their time fluctuations were not embedded in external events, and that the variations in shared understanding tend to precede 'individual attractions to the task' variations with seven team practical sessions. This study argues for further investigation of how 'togetherness' is experienced alternatively as a feeling of cohesion or shared understanding. Practitioner Summary: The present action research study investigated the experience that the team members have to share information during practice, and the subsequent benefices on team cohesion. Results call for specific interventions that make team members accept the fluctuating nature of team phenomena, to help them maintaining their daily efforts.

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Measuring Patterns in Team Interaction Sequences Using a Discrete Recurrence Approach

Abstract: Because they are not content based, and because they are automatic and fast, these new methods may be amenable to real-time communication pattern analysis.

Cited by 84 publications

References 31 publications

Examining Temporal Regularity in Categorical Team Communication Using Sample Entropy

Examining Temporal Regularity in Categorical Team Communication Using Sample Entropy

Understanding and Modeling Teams As Dynamical Systems

Fluctuations of the experience of togetherness within the team over time: task-cohesion and shared understanding throughout a sporting regular season

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