More than 30 years of research has firmly established the vital role of trust in human organizations and relationships, but the underlying mechanisms by which people build, lose, and rebuild trust remains incompletely understood. We propose a mechanistic model of trust that is grounded in the modern neuroscience of decision making. Since trust requires anticipating the future actions of others, any mechanistic model must be built upon up-to-date theories on how the brain learns, represents, and processes information about the future within its decision-making systems. Contemporary neuroscience has revealed that decision making arises from multiple parallel systems that perform distinct, complementary information processing. Each system represents information in different forms, and therefore learns via different mechanisms. When an act of trust is reciprocated or violated, this provides new information that can be used to anticipate future actions. The taxonomy of neural information representations that is the basis for the system boundaries between neural decision-making systems provides a taxonomy for categorizing different forms of trust and generating mechanistic predictions about how these forms of trust are learned and manifested in human behavior. Three key predictions arising from our model are (1) strategic risk-taking can reveal how to best proceed in a relationship, (2) human organizations and environments can be intentionally designed to encourage trust among their members, and (3) violations of trust need not always degrade trust, but can also provide opportunities to build trust.

翻译：过去30多年的研究已充分证实信任在人类组织与关系中的关键作用，但人们建立、丧失和重建信任的潜在机制仍未被完全理解。我们提出一个植根于现代决策神经科学的信任机制模型。由于信任需要预测他人未来行为，任何机制模型都必须建立在关于大脑如何在决策系统中学习、表征和处理未来信息的最新理论之上。当代神经科学揭示，决策产生于多个并行系统，这些系统执行不同但互补的信息处理。每个系统以不同形式表征信息，因此通过不同机制进行学习。当信任行为得到回报或遭受背叛时，这便提供了可用于预测未来行为的新信息。构成神经决策系统边界的神经信息表征分类学，为分类不同形式的信任以及生成关于这些信任形式如何学习并在人类行为中显现的机制性预测提供了基础框架。从我们的模型中衍生出的三个关键预测是：（1）战略性风险承担可以揭示如何在关系中取得最佳进展；（2）人类组织和环境可以有意识地设计以鼓励成员间的信任；（3）信任的违背并非总是损害信任，也可能为建立信任提供机遇。