Dynamical analysis in real time: detecting perturbations to team communication

Gorman, Jamie C.; Hessler, Eric E.; Amazeen, Polemnia G.; Cooke, Nancy J.; Shope, S.L.

doi:10.1080/00140139.2012.679317

Cited by 38 publications

(22 citation statements)

References 52 publications

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“…1 bottom; note, however, that we later apply smoothing to increase the robustness of peak identification). This window size is also more likely to capture intrinsic, as opposed to extrinsic, fluctuations in the communication patterns (Gorman, Hessler, et al, 2012).…”

Section: Sliding Window Entropymentioning

confidence: 99%

“…At issue is that the ordering and distribution of the states in the communication sequence changes over the duration of a task (e.g., Gorman, Hessler, et al, 2012). A sliding window calculation of entropy can capture fluctuations in the patterns of states that the system exhibits over time.…”

Section: Sliding Window Entropymentioning

confidence: 99%

“…These codes represent semantic categories of CPS processes (i.e., information provision, solution option generation, etc.). The sequential concatenation of these codes, derived from observation of a CPS task, form a discrete, nominal time series Gorman, Hessler, Amazeen, Cooke, & Shope, 2012). As such, to derive a continuous measure of entropy on this type of time series, a windowed entropy procedure was used (Gorman et al, 2016;Likens, Amazeen, Stevens, Galloway, & Gorman, 2014).…”

Section: Overview Of the Present Studymentioning

confidence: 99%

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Problem‐Solving Phase Transitions During Team Collaboration

2017

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Multiple theories of problem-solving hypothesize that there are distinct qualitative phases exhibited during effective problem-solving. However, limited research has attempted to identify when transitions between phases occur. We integrate theory on collaborative problem-solving (CPS) with dynamical systems theory suggesting that when a system is undergoing a phase transition it should exhibit a peak in entropy and that entropy levels should also relate to team performance. Communications from 40 teams that collaborated on a complex problem were coded for occurrence of problem-solving processes. We applied a sliding window entropy technique to each team's communications and specified criteria for (a) identifying data points that qualify as peaks and (b) determining which peaks were robust. We used multilevel modeling, and provide a qualitative example, to evaluate whether phases exhibit distinct distributions of communication processes. We also tested whether there was a relationship between entropy values at transition points and CPS performance. We found that a proportion of entropy peaks was robust and that the relative occurrence of communication codes varied significantly across phases. Peaks in entropy thus corresponded to qualitative shifts in teams' CPS communications, providing empirical evidence that teams exhibit phase transitions during CPS. Also, lower average levels of entropy at the phase transition points predicted better CPS performance. We specify future directions to improve understanding of phase transitions during CPS, and collaborative cognition, more broadly.

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Section: Sliding Window Entropymentioning

confidence: 99%

Section: Sliding Window Entropymentioning

confidence: 99%

Section: Overview Of the Present Studymentioning

confidence: 99%

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Problem‐Solving Phase Transitions During Team Collaboration

2017

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“…Breakdowns and unexpected changes in these processes are at least partially responsible for the Challenger Shuttle disaster (Vaughan, 1996), delayed response times to Hurricane Katrina (Leonard and Howitt, 2006), and poor communication in air traffic control in response to the September 11th attacks (Kean and Hamilton, 2004). For team assessment, it is important to detect these breakdowns and roadblocks as they unfold in real-time (Gorman et al, 2012b). …”

Section: Practical Implications For Team Training and Assessmentmentioning

confidence: 99%

“…The assumption behind real-time dynamics is that we can meaningfully analyze team interaction data ad hoc , as it becomes available, as opposed to post hoc (Gorman et al, 2012b). This is plausible due to the “historical” quality of team interaction, such that team communication has long-memory.…”

Section: Practical Implications For Team Training and Assessmentmentioning

confidence: 99%

Understanding and Modeling Teams As Dynamical Systems

et al. 2017

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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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Toward the incorporation of temporal interaction analysis techniques in modeling and understanding sociotechnical systems

David

Schraagen

Endedijk

2021

Hum Ftrs & Erg Mfg Svc

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The increased complexity of modern sociotechnical systems (STS) necessitates the need for a manageable representation of their attributes, to augment our understanding and enable the development of ways through which we can increase their effectiveness, efficiency, and safety. Although many of the methodologies developed in the Human Factors domain map and investigate system properties and network structures, the inclusion of the temporal dimension in the analysis of STS remains limited. In this paper we present how modeling and visualization of STS can be augmented with the incorporation of temporal interaction analysis techniques that enable a micro-level, fine-grained analysis of data. We provide an overview of temporal analysis techniques by breaking down their main function, requirements, types of research questions they can address, and the visualization properties they offer, attempting to enhance their use in system analysis. This overview can assist researchers in selecting an analysis technique, enabling the incorporation of temporality in STS analysis, and helping towards the design of improved and safer systems and interventions.

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Dynamical analysis in real time: detecting perturbations to team communication

Cited by 38 publications

References 52 publications

Problem‐Solving Phase Transitions During Team Collaboration

Problem‐Solving Phase Transitions During Team Collaboration

Understanding and Modeling Teams As Dynamical Systems

Toward the incorporation of temporal interaction analysis techniques in modeling and understanding sociotechnical systems

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