Individualized Prediction of Third-Party Punishment Behavior from Intrinsic Functional Brain Connectivity



Hsu, Ko-Tsung

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A robust human society is developed normally on the ground of social cooperation, serving a critical role in human relationships. Importantly, social cooperation is subjected to the establishment of social norms. To maintain human society, third-party punishment (TPP) as a consistently sanctioning behavior facilitates the enforcement of social norms. At the psychological level, TPP is based on blame which is an amalgam of intent and harm to victim and the offender’s intention in violating social norms. At the neural level, TPP behavior builds on the interaction of the salience network (determining the degree of norm violation), default-mode network (determining the degree of blame), and central-executive network (determining the degree of punishment). Although task-based functional magnetic resonance imaging (fMRI) has been extensively used to investigate individual differences in the propensity to punish, whether TPP behavior can be predicted through task-free fMRI based on resting-state functional connectivity (RSFC) remains open. The goal of this study was to apply multivariate prediction analysis (MVPA) to RSFC patterns of large-scale ix brain networks to predict individual difference in TPP behavior measured with a TPP exchange game. The findings demonstrated that RSFC between the default-mode network (DMN) and the central-executive network (CEN) predicted TPP behavior, indicating a signal transmission from blame (DMN) to punishment behavior (CEN). In conclusion, investigating the individual difference in TPP behavior based on RSFC provides us with a new comprehensive understanding of sustaining cooperation and enforcement of social norms in human society.



Resting-state functional connectivity, Third-party punishment, Economic game, Multivariate prediction analysis, Brain connectivity, Social norm