ReviewFrontiers in psychology2017
Understanding and Modeling Teams As Dynamical Systems.
Review in Frontiers in psychology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 88 citations in OpenAlex.
- A Methodological Framework to Study Change in Team Cognition Under the Dynamical Hypothesis.Topics in cognitive science · 2026Article
- Investigating interbrain synchrony under teamwork disruption: an fNIRS hyperscanning study.Behavioral and brain functions : BBF · 2026Article
- The dynamics of leadership and success in software development teams.Nature communications · 2025Article
- Physiological team dynamics explored: physiological synchrony in medical simulation training.Advances in simulation (London, England) · 2025Article
- From Teams to Teamness: Future Directions in the Science of Team Cognition.Human factors · 2024Review
- Exceeding the Ordinary: A Framework for Examining Teams Across the Extremeness Continuum and Its Impact on Future Research.Group & organization management · 2023Article
- An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action.Frontiers in human neuroscience · 2023Article
- The dynamics of dyadic interactions between people of different ethnicities depend on their identification with all humanity.Scientific reports · 2022Article
- How to orchestrate a soccer team: Generalized synchronization promoted by rhythmic acoustic stimuli.Frontiers in human neuroscience · 2022Article
- Collective intelligence in teams: Contextualizing collective intelligent behavior over time.Frontiers in psychology · 2022Review
- An Active Inference Model of Collective Intelligence.Entropy (Basel, Switzerland) · 2021Article
- Losing the Home Field Advantage When Playing Behind Closed Doors During COVID-19: Change or Chance?Frontiers in psychology · 2021Article
- Using Communication to Modulate Neural Synchronization in Teams.Frontiers in human neuroscience · 2020Article
- Antifragility in Climbing: Determining Optimal Stress Loads for Athletic Performance Training.Frontiers in psychology · 2020Article
- Physiological synchronization and entropy as measures of team cognitive load.Journal of biomedical informatics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.