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Wiltshire TJ, van Eijndhoven K, Halgas E, Gevers JMP. Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams. Top Cogn Sci 2024; 16:391-429. [PMID: 35261211 DOI: 10.1111/tops.12606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/02/2022] [Accepted: 02/04/2022] [Indexed: 11/26/2022]
Abstract
Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.
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Affiliation(s)
- Travis J Wiltshire
- Department of Cognitive Science and Artificial Intelligence, Tilburg University
| | | | - Elwira Halgas
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology
| | - Josette M P Gevers
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology
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Chuang TM, Peng PC, Su YK, Lin SH, Tseng YL. Exploring Inter-Brain Electroencephalogram Patterns for Social Cognitive Assessment During Jigsaw Puzzle Solving. IEEE Trans Neural Syst Rehabil Eng 2024; 32:422-430. [PMID: 38198273 DOI: 10.1109/tnsre.2024.3352036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Social interaction enables the smooth progression of our daily lives. Mounting evidence from recent hyperscanning neuroimaging studies indicates that key components of social behavior can be evaluated using inter-brain oscillations and connectivity. However, mapping out inter-brain networks and developing neurocognitive theories that explain how humans co-create and share information during social interaction remains challenging. In this study, we developed a jigsaw puzzle-solving game with hyperscanning electroencephalography (EEG) signals recorded to investigate inter-brain activities during social interactions involving cooperation and competition. Participants were recruited and paired into dyads to participate in the multiplayer jigsaw puzzle game with 32-channel EEG signals recorded. The corresponding event-related potentials (ERPs), brain oscillations, and inter-brain functional connectivity were analyzed. The results showed different ERP morphologies of P3 patterns in competitive and cooperative contexts, and brain oscillations in the low-frequency band may be an indicator of social cognitive activities. Furthermore, increased inter-brain functional connectivity in the delta, theta, alpha, and beta frequency bands was observed in the competition mode compared to the cooperation mode. By presenting comparable and valid hyperscanning EEG results alongside those of previous studies using traditional paradigms, this study demonstrates the potential of utilizing hyperscanning techniques in real-life game-playing scenarios to quantitatively assess social cognitive interactions involving cooperation and competition. Our approach offers a promising platform with potential applications in the flexible assessment of psychiatric disorders related to social functioning.
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Hakim U, De Felice S, Pinti P, Zhang X, Noah JA, Ono Y, Burgess PW, Hamilton A, Hirsch J, Tachtsidis I. Quantification of inter-brain coupling: A review of current methods used in haemodynamic and electrophysiological hyperscanning studies. Neuroimage 2023; 280:120354. [PMID: 37666393 DOI: 10.1016/j.neuroimage.2023.120354] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023] Open
Abstract
Hyperscanning is a form of neuroimaging experiment where the brains of two or more participants are imaged simultaneously whilst they interact. Within the domain of social neuroscience, hyperscanning is increasingly used to measure inter-brain coupling (IBC) and explore how brain responses change in tandem during social interaction. In addition to cognitive research, some have suggested that quantification of the interplay between interacting participants can be used as a biomarker for a variety of cognitive mechanisms aswell as to investigate mental health and developmental conditions including schizophrenia, social anxiety and autism. However, many different methods have been used to quantify brain coupling and this can lead to questions about comparability across studies and reduce research reproducibility. Here, we review methods for quantifying IBC, and suggest some ways moving forward. Following the PRISMA guidelines, we reviewed 215 hyperscanning studies, across four different brain imaging modalities: functional near-infrared spectroscopy (fNIRS), functional magnetic resonance (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG). Overall, the review identified a total of 27 different methods used to compute IBC. The most common hyperscanning modality is fNIRS, used by 119 studies, 89 of which adopted wavelet coherence. Based on the results of this literature survey, we first report summary statistics of the hyperscanning field, followed by a brief overview of each signal that is obtained from each neuroimaging modality used in hyperscanning. We then discuss the rationale, assumptions and suitability of each method to different modalities which can be used to investigate IBC. Finally, we discuss issues surrounding the interpretation of each method.
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Affiliation(s)
- U Hakim
- Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, United Kingdom.
| | - S De Felice
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Department of Psychology, University of Cambridge, United Kingdom
| | - P Pinti
- Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, United Kingdom; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom
| | - X Zhang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
| | - J A Noah
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
| | - Y Ono
- Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University, Kawasaki, Kanagawa, Japan
| | - P W Burgess
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - A Hamilton
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - J Hirsch
- Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, United Kingdom; Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States; Departments of Neuroscience and Comparative Medicine, Yale School of Medicine, New Haven, CT, United States; Yale University, Wu Tsai Institute, New Haven, CT, United States
| | - I Tachtsidis
- Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, United Kingdom
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van Eijndhoven K, Wiltshire TJ, Hałgas EA, Gevers JMP. A Methodological Framework to Study Change in Team Cognition Under the Dynamical Hypothesis. Top Cogn Sci 2023. [PMID: 37643357 DOI: 10.1111/tops.12685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/18/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023]
Abstract
The dynamical hypothesis claims that cognitive systems, such as teams, are dynamical systems (i.e., an interdependent collection of individuals and their technology that change together over time). Following this hypothesis, team researchers have adopted dynamical approaches to better understand the team cognitive processes and states that form team cognition, as well as how they emerge over time. One approach focuses on team coordination dynamics, which examines the coupling of signals between interacting individuals in various modalities, and has been shown to reflect aspects of team functioning including team cognition. However, how changes in team coordination relate to high-level team cognitive processes and states, as well as important events, are not yet fully understood. To this end, we advance a methodological framework for researching team cognition under the dynamical hypothesis. Subsequently, we provided an empirical case-study application of this framework. Thereby, this work contributes methodologically and empirically to a deeper understanding of team cognition, the dynamical hypothesis, and the synergy between them.
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Affiliation(s)
| | - Travis J Wiltshire
- Department of Cognitive Science and Artificial Intelligence, Tilburg University
| | - Elwira A Hałgas
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology
| | - Josette M P Gevers
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology
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Hałgas EA, van Eijndhoven KHJ, Gevers JMP, Wiltshire TJ, M. Westerink JHD, Rispens S. A Review of Using Wearable Technology to Assess Team Functioning and Performance. SMALL GROUP RESEARCH 2022. [DOI: 10.1177/10464964221125717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Wearable technology enables collecting continuous in situ data from multiple people in various modalities, which can enhance team research and support, as the dynamic coupling of signals between interacting individuals (i.e., team coordination dynamics) is believed to reflect underlying processes and states of team functioning and performance. We conducted a systematic review on existing literature to evaluate the prospective use of wearable technology in research and practice. Using the IMOI framework as an organizing tool, our review revealed considerable support linking team coordination dynamics in different modalities to team functioning and performance, but also explicated the field’s nascent status.
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Affiliation(s)
| | | | | | | | - Joyce H. D. M. Westerink
- Eindhoven University of Technology, The Netherlands
- Digital Engagement, Cognition and Behavior Group, Philips Research, Eindhoven, The Netherlands
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Immler S, Rappelsberger P, Baca A, Exel J. Guardiola, Klopp, and Pochettino: The Purveyors of What? The Use of Passing Network Analysis to Identify and Compare Coaching Styles in Professional Football. Front Sports Act Living 2021; 3:725554. [PMID: 34746774 PMCID: PMC8569793 DOI: 10.3389/fspor.2021.725554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
We applied social networks analysis to objectively discriminate and describe interpersonal interaction dynamics of players across different top-coaching styles. The aim was to compare metrics in the passing networks of Jürgen Klopp, Pep Guardiola, and Mauricio Pochettino across the UEFA Champions League seasons from 2017 to 2020. Data on completed passes from 92 games were gathered and average passing networks metrics were computed. We were not only able to find the foundations on which these elite coaches build the passing dynamics in their respective teams, but also to determine important differences that represent their particular coaching signatures. The local cluster coefficient was the only metric not significantly different between coaches. Still, we found higher average shortest-path length for Guardiola's network (mean ± std = 3.00 ± 0.45 a.u.) compared to Klopp's (2.80 ± 0.52 a.u., p = 0.04) and Pochettino's (2.70 ± 0.39 a.u., p = 0.01). Density was higher for Guardiola's (64.16 ± 20.27 a.u.) than for Pochettino's team (51.42 ± 17.28 a.u., p = 0.008). The largest eigenvalue for Guardiola's team (65.95 ± 16.79 a.u.) was higher than for Klopp's (47.06 ± 17.25 a.u., p < 0.001) and Pochettino's (42,62 ± 12.01 a.u., p < 0.001). Centrality dispersion was also higher for Guardiola (0.14 ± 0.02 a.u.) when compared to Klopp (0.12 ± 0.03 a.u., p = 0.008). The local cluster coefficient seems to build the foundation for passing work, however, cohesion characteristics among players in the three teams of the top coaches seems to characterize their own footprint regarding passing dynamics. Guardiola stands out by the high number of passes and the enhanced connection of the most important players in the network. Klopp and Pochettino showed important similarities, which are associated to preferences toward more flexibility of interpersonal linkages synergies.
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Affiliation(s)
- Sebastian Immler
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Philipp Rappelsberger
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Arnold Baca
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
| | - Juliana Exel
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
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Tognoli E, Zhang M, Fuchs A, Beetle C, Kelso JAS. Coordination Dynamics: A Foundation for Understanding Social Behavior. Front Hum Neurosci 2020; 14:317. [PMID: 32922277 PMCID: PMC7457017 DOI: 10.3389/fnhum.2020.00317] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 07/17/2020] [Indexed: 11/13/2022] Open
Abstract
Humans' interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB's evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights.
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Affiliation(s)
- Emmanuelle Tognoli
- Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, United States
- Department of Biological Sciences, Florida Atlantic University, Boca Raton, FL, United States
| | - Mengsen Zhang
- Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Armin Fuchs
- Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, United States
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
| | - Christopher Beetle
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
| | - J. A. Scott Kelso
- Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, United States
- Intelligent Systems Research Centre, Ulster University, Londonderry, United Kingdom
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