Prediction of Individual Variation of Second-party Punishment from Resting-state Functional Connectivity



Ding, Fengying

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Social norms and associated altruistic behaviors are significant for human society. Humans are willing to punish the violators of social norms at their personal costs (i.e., costly secondparty punishment, SPP), which can be measured with socio-economic exchange games. From the view of psychology, SPP is driven by blame, integrating the harm of victim and intent of offender. From the perspective of neural network, SPP behavior is associated with salience network, default-mode network and central-executive network (CEN). Although SPP is associated with large-scale brain networks regulating social-cognitive processes measured with task-based functional magnetic resonance imaging (fMRI), the prediction of individual variation of SPP behavior based on resting-state functional connectivity (RSFC) measured with task-free fMRI has not yet been established. The aim of this master thesis was to predict individual differences in SPP—measured via a two-person economic exchange game— based on RSFC combining task-free fMRI with a multivariate prediction analysis (MVPA). First, we showed on the behavioral level that SPP increased with the degree of unfair offers in the SPP game. Second, we demonstrated on the neural level, that variation in average SPP behavior was predicted through RSFC within the centralexecutive network confirming that CEN is the driving network for the determination of SPP behavior. In conclusion, our study provides a comprehensive picture regarding SPP behavior for maintaining human social norms.



Resting-state fMRI, Multivariate analysis, Second-party punishment, Prediction