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Leader–follower behavioural coordination and neural synchronization during intergroup conflict

Nature Human Behaviour volume 7, pages 2169–2181 (2023)Cite this article

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Abstract

Leaders can launch hostile attacks on out-groups and organize in-group defence. Whether groups settle the conflict in their favour depends, however, on whether followers align with leader’s initiatives. Yet how leader and followers coordinate during intergroup conflict remains unknown. Participants in small groups elected a leader and made costly contributions to intergroup conflict while dorsolateral prefrontal cortex (DLPFC) activity was simultaneously measured. Leaders were more sacrificial and their contribution influenced group survival to a greater extent during in-group defence than during out-group attacks. Leaders also had increased DLPFC activity when defending in-group, which predicted their comparatively strong contribution to conflict; followers reciprocated their leader’s initiatives the more their DLPFC activity synchronized with that of their leader. When launching attacks, however, leaders and followers aligned poorly at behavioural and neural levels, which explained why out-group attacks often failed. Our results provide a neurobehavioural account of leader–follower coordination during intergroup conflict and reveal leader–follower behavioural/neural alignment as pivotal for groups settling conflicts in their favour.

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Fig. 1: Experimental settings and behavioural results.
Fig. 2: Interpersonal neural synchronization between leaders and followers during defence and attack.
Fig. 3: Intra-individual activity in the rDLPFC during intergroup conflict.
Fig. 4: Prediction of behavioural alignment.

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Data availability

All behavioural data and materials have been made publicly available via the Open Science Framework and can be accessed at https://osf.io/7grfu/. The neural data supporting the main findings of this study are available from the corresponding author upon request.

Code availability

The custom routines for the main data analysis written in MATLAB are available in the Open Science Framework at https://osf.io/7grfu/.

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Acknowledgements

We thank C. Yang and X. Zou for assistance in data collection. This work was supported by the National Natural Science Foundation of China (Project 32125019 to Y.M.); the STI 2030—Major Projects 2022ZD0211000 to Y.M.; the Major Project of National Social Science Foundation (19ZDA363 to Y.M.); the Fundamental Research Funds for the Central Universities (to Y.M.); the start-up funding from the State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University (to Y.M.) and the Spinoza Award from the Netherlands Science Foundation (NWO SPI-57-242 to C.K.W.D.D.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Hejing Zhang, Jiaxin Yang, Jun Ni & Yina Ma

  2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

    Jiaxin Yang, Jun Ni & Yina Ma

  3. Social, Economic, and Organizational Psychology, Leiden University, Leiden, the Netherlands

    Carsten K. W. De Dreu

  4. Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands

    Carsten K. W. De Dreu

  5. Center for Research in Experimental Economics and Political Decision Making, Amsterdam School of Economics, University of Amsterdam, Amsterdam, the Netherlands

    Carsten K. W. De Dreu

  6. Chinese Institute for Brain Research, Beijing, China

    Yina Ma

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Contributions

Y.M. and C.K.W.D.D. conceived the project and designed the experiment; H.Z., J.Y. and J.N. implemented the experiment and collected data; H.Z. and J.Y. analysed the data under the supervision of Y.M.; Y.M. and C.K.W.D.D. interpreted the results; Y.M. wrote the original and final version of the manuscript; J.Y. and C.K.W.D.D. provided critical revisions. All authors approved the final version of the manuscript for submission.

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Correspondence to Yina Ma.

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Extended data

Extended Data Fig. 1 Verify frequency band with increased neural synchronization to intergroup contest than resting-state.

To validate that the timeline-based frequency band of interest, we averaged coherence values across all 14 channels and the averaged coherence value during the resting-state was subtracted from that of inter-group contest task. We performed one sample t-tests on the coherence value difference for each of the 121 frequency bands (frequency range: 0.004 to 4.547 Hz). Frequency band from 0.0282 Hz to 0.0335 Hz (corresponding to the period between 29.82s and 35.47s) showed significantly increased coherence value of inter-group contest (vs. resting state, p < 0.05, corrected by False discovery rate (FDR) for 121 frequency bands), which was overlapping with the timeline-based frequency band of interest (that is, frequency band from 0.0263 HZ to 0.0357 HZ). Moreover, the coherence value within the timeline-identified frequency band of interest also showed significant increase during inter-group contest than the resting-state (Mean ± SE = 0.011 ± 0.004, t79 = 2.963, p = 0.004, Cohen’s d = 0.331).

Extended Data Fig. 2 Neural synchronization between the group leader and a randomly selected follower.

There were significant leader-by-role interaction when considering neural synchronization of the leader and a randomly selected follower, rather than the across pair averages (a: channel 11, F1,78 = 9.230, p = 0.003, η2p = 0.106; b: channel 6, F1,78 = 9.219, p = 0.003, η2p = 0.106). Data are shown as the mean ± s.e. with overlaid dot plots. n = 80 three-versus-three-person intergroup contest sessions. **p < 0.01.

Extended Data Fig. 3 Validation of neural synchronization.

(a) We generated pseudo groups by randomly grouping a real leader and two real followers from different original groups to a pseudo group. We then re-calculated neural synchronization of pseudo groups using the same method as we did for the real pairs. We repeated the generation of pseudo groups and recalculation of neural synchronization for 1000 times. We verified stronger neural synchronization in real interacting leader-follower pairs of defender groups than pseudo groups (b, p = 0.003), but not for follower-follower pairs of defender groups (c, p = 0.981), nor leader-follower (d, p = 0.711), follower-follower (e, p = 0.590) pairs of attacker groups.

Extended Data Fig. 4 Validation of neural synchronization.

(a) We kept each original group and generated within-group pseudo pairs for each group by randomly assigning one of the two followers as a pseudo leader. We then re-calculated neural synchronization of pseudo pairs using the same method as we did for the real pairs. We repeated the generation of pseudo pairs and recalculation of neural synchronization for 1000 times. We verified stronger neural synchronization in real interacting leader-follower pairs of defender groups than pseudo groups (b, p = 0.001), but not for follower-follower pairs of defender groups (c, p = 0.999), nor leader-follower (d, p = 0.450), follower-follower (e, p = 0.420) pairs of attacker groups.

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Zhang, H., Yang, J., Ni, J. et al. Leader–follower behavioural coordination and neural synchronization during intergroup conflict. Nat Hum Behav 7, 2169–2181 (2023). https://doi.org/10.1038/s41562-023-01663-0

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