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Spontaneous neural encoding of social network position

Nature Human Behaviour volume 1, Article number: 0072 (2017) Cite this article

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Abstract

Unlike many species that enact social behaviour in loose aggregations (such as swarms or herds), humans form groups comprising many long-term, intense, non-reproductive bonds with non-kin1. The cognitive demands of navigating such groups are thought to have significantly influenced human brain evolution2. Yet little is known about how and to what extent the human brain encodes the structure of the social networks in which it is embedded. We characterized the social network of an academic cohort (N = 275); a subset (N = 21) completed a functional magnetic resonance imaging (fMRI) study involving viewing individuals who varied in terms of ‘degrees of separation’ from themselves (social distance), the extent to which they were well-connected to well-connected others (eigenvector centrality) and the extent to which they connected otherwise unconnected individuals (brokerage). Understanding these characteristics of social network position requires tracking direct relationships, bonds between third parties and the broader network topology. Pairing network data with multi-voxel pattern analysis, we show that information about social network position is accurately perceived and spontaneously activated when encountering familiar individuals. These findings elucidate how the human brain encodes the structure of its social world and underscore the importance of integrating an understanding of social networks into the study of social perception.

Relationships are intrinsic to human behaviour. Everyday interactions are shaped not only by our own relationships, but also by knowledge of bonds between third parties and the broader social networks in which we are embedded. Well-connected individuals can effectively threaten or bolster one’s reputation3, those who bridge otherwise disparate groups can efficiently seek and spread information4, and knowledge of mutual ties influences information-sharing and trust5. Human social intelligence rests, in part, on a calculus that inheres in an understanding of social network structure.

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Figure 1: Social network characterization.
Figure 2: Stimulus set construction and paradigm for neuroimaging study.
Figure 3: GLM decomposition searchlight.
Figure 4: Neural encoding of social network position.

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Acknowledgements

This work was supported by a graduate fellowship from the Neukom Institute for Computational Science and a Dartmouth Graduate Alumni Research Award to C.P. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors thank W. Haslett for assistance with the optical flow analysis.

Author information

Authors and Affiliations

  1. Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, 90095, California, USA

    Carolyn Parkinson

  2. Tuck School of Business, Dartmouth College, 100 Tuck Hall, Hanover, 03755, New Hampshire, USA

    Adam M. Kleinbaum

  3. Department of Psychological and Brain Sciences, Dartmouth College, 6207 Moore Hall, Hanover, 03755, New Hampshire, USA

    Thalia Wheatley

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  1. Carolyn Parkinson
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  2. Adam M. Kleinbaum
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  3. Thalia Wheatley
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Contributions

C.P., A.M.K. and T.W. conceived and designed the study. C.P. and A.M.K. collected the data. C.P. analysed the data. C.P., A.M.K. and T.W. wrote the paper.

Corresponding author

Correspondence to Carolyn Parkinson.

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The authors declare no competing interests.

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Supplementary Figures 1–4, Supplementary Tables 1–3, Supplementary Methods, Supplementary References. (PDF 586 kb)

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Parkinson, C., Kleinbaum, A. & Wheatley, T. Spontaneous neural encoding of social network position. Nat Hum Behav 1, 0072 (2017). https://doi.org/10.1038/s41562-017-0072

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