Affective Brain Lab, Experimental Psychology, University College London, London, United Kingdom.
Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
PLoS Comput Biol. 2022 May 2;18(5):e1010010. doi: 10.1371/journal.pcbi.1010010. eCollection 2022 May.
Social interactions influence people's feelings and behavior. Here, we propose that a person's well-being is influenced not only by interactions they experience themselves, but also by those they observe. In particular, we test and quantify the influence of observed selfishness and observed inequality on a bystanders' feelings and non-costly punishment decisions. We developed computational models that relate others' (un)selfish acts to observers' emotional reactions and punishment decisions. These characterize the rules by which others' interactions are transformed into bystanders' reactions, and successfully predict those reactions in out-of-sample participants. The models highlight the impact of two social values-'selfishness aversion' and 'inequality aversion'. As for the latter we find that even small violations from perfect equality have a disproportionately large impact on feelings and punishment. In this age of internet and social media we constantly observe others' online interactions, in addition to in-person interactions. Quantifying the consequences of such observations is important for predicting their impact on society.
社交互动会影响人们的感受和行为。在这里,我们提出,一个人的幸福感不仅受到他们自己经历的互动的影响,还受到他们观察到的互动的影响。具体来说,我们测试和量化了观察到的自私和观察到的不平等对旁观者的感受和非成本惩罚决策的影响。我们开发了计算模型,将他人的(不)自私行为与观察者的情绪反应和惩罚决策联系起来。这些模型描述了将他人的互动转化为旁观者反应的规则,并成功地预测了样本外参与者的反应。这些模型突出了两种社会价值观——“厌恶自私”和“厌恶不平等”的影响。就后者而言,我们发现,即使是与完全平等略有偏离,也会对情感和惩罚产生不成比例的巨大影响。在互联网和社交媒体时代,我们不仅在现实生活中观察他人的互动,也在网络上观察他人的互动。量化这些观察的后果对于预测它们对社会的影响很重要。