Another in need enhances prosociality and modulates frontal theta oscillations in young adults

Abstract

IntroductionDecision-making is a process that can be strongly affected by social factors. Evidence has shown how people deviate from traditional rational-choice predictions under different levels of social interactions. The emergence of prosocial decision-making, defined as any action that is addressed to benefit another individual even at the expense of personal benefits, has been reported as an example of such social influence. Furthermore, brain evidence has shown the involvement of structures such as the prefrontal cortex, anterior insula, and midcingulate cortex during decision settings in which a decision maker interacts with others under physical pain or distress or while being observed by others.MethodsUsing a slightly modified version of the dictator game and EEG recordings, we tested the hypothesis that the inclusion of another person into the decision setting increases prosocial decisions in young adults and that this increase is higher when the other person is associated with others in need. At the brain level, we hypothesized that the increase in prosocial decisions correlates with frontal theta activity.Results and DiscussionThe results showed that including another person in the decision, setting increased prosocial behavior only when this presence was associated with someone in need. This effect was associated with an increase in frontocentral theta-oscillatory activity. These results suggest that the presence of someone in need enhances empathy concerns and norm compliance, raising the participants’ prosocial decision-making.

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“…The results were presented in the figures depicting the posterior distribution of the parameters and the p MCMC derived from this analysis (p MCMC is a p-value derived by comparing the posterior distributions of the estimated parameters sampled via Markov Chain Monte Carlo; see section "Cognitive modeling" for details). Additionally, for testing behavioral indicators in the Reversal Learning Task, we employed non-parametric statistics (since these indicators generally have a non-normal distribution [39][40][41][42][43] ) and their respective effect size measures, together with mixed linear models.…”