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A causal account of the brain network computations underlying strategic social behavior

Nature Neuroscience volume 20, pages 1142–1149 (2017)Cite this article

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Abstract

During competitive interactions, humans have to estimate the impact of their own actions on their opponent's strategy. Here we provide evidence that neural computations in the right temporoparietal junction (rTPJ) and interconnected structures are causally involved in this process. By combining inhibitory continuous theta-burst transcranial magnetic stimulation with model-based functional MRI, we show that disrupting neural excitability in the rTPJ reduces behavioral and neural indices of mentalizing-related computations, as well as functional connectivity of the rTPJ with ventral and dorsal parts of the medial prefrontal cortex. These results provide a causal demonstration that neural computations instantiated in the rTPJ are neurobiological prerequisites for the ability to integrate opponent beliefs into strategic choice, through system-level interaction within the valuation and mentalizing networks.

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Figure 1: Experimental design and model evaluation.
Figure 2: Effects of rTPJ-cTBS on behavior.
Figure 3: Stimulation site and local neural effects of rTPJ-cTBS.
Figure 4: rTPJ-cTBS effects on neural activity in dmPFC.
Figure 5: rTPJ-cTBS effects on value coding in vmPFC.
Figure 6: Relationship between neural effects of cTBS and model parameters.

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Acknowledgements

This study was supported by grants from the Swiss National Science Foundation (CRSII3_141965 and 105314_152891) to C.C.R. We thank the staff of the Laboratory for Social and Neural Systems Research for practical support.

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Authors and Affiliations

  1. Department of Economics, Laboratory for Social and Neural Systems Research (SNS-Lab), University of Zurich, Zurich, Switzerland

    Christopher A Hill, Rafael Polania, Marius Moisa & Christian C Ruff

  2. Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Miyagi, Japan

    Shinsuke Suzuki

  3. Institute of Development, Aging and Cancer, Tohoku University, Sendai, Miyagi, Japan

    Shinsuke Suzuki

  4. Biomedical Engineering, University and ETH of Zurich, Zurich, Switzerland

    Marius Moisa

  5. Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA

    John P O'Doherty

  6. Computation and Neural Systems, California Institute of Technology, Pasadena, California, USA

    John P O'Doherty

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  1. Christopher A Hill
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Contributions

C.A.H. designed the study, collected data, performed analysis and wrote manuscript. S.S. performed analysis and contributed to the manuscript. R.P. performed analysis and contributed to the manuscript. M.M. contributed to TMS–fMRI procedure, collected data and contributed to the manuscript. J.P.O. designed the study and contributed to the manuscript. C.C.R. designed the study, contributed to data analysis and wrote the manuscript.

Corresponding authors

Correspondence to Christopher A Hill or Christian C Ruff.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Hierarchical Bayesian influence model

Graphical representation of the Hierarchical Bayesian influence model fitted to the data for each subject. Clear shapes indicate latent variables and filled shapes observed variables (in this case, the choice of the subject/employee Chyee and the choice of the opponent/employer Chyer). The index t denotes trial and s denotes subject. The same procedure was applied for fictitious play and Reinforcement learning where μ(s,t) depends only on Chyer and follows equation [8] and [3] respectively.

Supplementary Figure 2 Deviation from random responding

The population-level parameter P(Work) deviates from the Mixed-Nash Equilibrium of 0.5 for vertex-cTBS (pmcmc ≅ 0) and rTPJ-cTBS (pmcmc ≅ 0). Standard deviations are shown in black.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2 and Supplementary Tables 1–3 (PDF 528 kb)

Supplementary Methods Checklist (PDF 601 kb)

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Hill, C., Suzuki, S., Polania, R. et al. A causal account of the brain network computations underlying strategic social behavior. Nat Neurosci 20, 1142–1149 (2017). https://doi.org/10.1038/nn.4602

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