Abstract:
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.
