Behavioural Science Institute, Radboud University, Nijmegen, GE, Netherlands.
Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, GE, Netherlands.
PLoS One. 2024 Apr 4;19(4):e0300886. doi: 10.1371/journal.pone.0300886. eCollection 2024.
When deciding whether to reciprocate trust, people are typically strongly influenced by how much trust their interaction partner has originally shown them. If a partner has placed a lot of trust in you, there is a strong motivation to reciprocate, and indeed this factor often outweighs pro-self considerations to maximize one's own financial payout. However, one important unanswered question in this regard is what people decide to do when this prior information is ambiguous; that is, when they do not know for sure exactly how trusting their partner has been. How then do people decide to reciprocate? This study utilizes a novel version of the Trust Game to directly address this question. Here, we develop, and validate, a computational model-based approach to quantify and categorize how participants assessed the trustworthiness of an unfamiliar partner when making reciprocity decisions. We find that participants spontaneously use their prior experience about the trustingness of game partners in general to inform their reciprocity decisions, even when they had the opportunity to strategically assume that their new, unfamiliar, partners were untrusting, and hence could have justified lower reciprocation rates.
当决定是否回报信任时，人们通常会受到交互伙伴最初向他们展示的信任程度的强烈影响。如果一个伙伴对你非常信任，那么回报信任的动机就非常强烈，事实上，这一因素往往超过了自利考虑，以最大化自己的经济收益。然而，在这方面，一个重要的未解决问题是，当这种先验信息不明确时，人们会决定做什么；也就是说，当他们不确定他们的伙伴到底有多信任他们时。那么人们如何决定回报呢？本研究利用一种新颖的信任游戏版本，直接解决这个问题。在这里，我们开发并验证了一种基于计算模型的方法，用于量化和分类参与者在做出互惠决策时如何评估陌生伙伴的可信任程度。我们发现，即使参与者有机会从策略上假设他们的新的、不熟悉的伙伴是不值得信任的，因此可以 justifies 较低的互惠率，他们也会自发地利用他们以前关于游戏伙伴信任度的经验来告知他们的互惠决策。
