基于神经信息处理的信任机制模型
A mechanistic model of trust based on neural information processing
AI总结：
该研究基于决策认知神经科学提出信任机制模型，将信任与神经决策系统关联，统一多学科发现并为信任的发展、维持等提供神经层面预测。
摘要：
信任是人类社会互动的核心，表现为在采取使自身易受他人影响的行动时的关键信息处理步骤。我们认为信任依赖于神经系统中产生的决策过程。基于决策认知神经科学的进展，我们提出了一种信任机制模型，该模型在多个并行系统中以不同方式产生，这些系统执行着不同且互补的信息处理。由于每个系统通过不同的计算机制学习，它们与环境的互动方式也不同，信任可以通过多种方式产生（或破坏）。这种信息表征的系统级分类为区分信任形式、将其与特定学习过程关联以及对其在行为中的表现产生可测试预测提供了原则性基础。此外，由于这些不同的计算过程由不同的神经回路实现，我们的理论对不同类型信任背后的不同神经回路做出了可测试预测。通过将信任置于更广泛的神经决策系统理论中，我们的解释统一了心理学、神经科学和社会科学的各种发现，并为解释人类如何在复杂社会世界中发展、维持、丧失和修复信任提供了基础。
英文摘要：
Trust is central to human social interactions, manifesting as a critical information processing step in taking actions that make one vulnerable to another. We argue that trust depends on the decision-making processes that arise in neural systems. Building on advances in the cognitive neuroscience of decision making, we propose a mechanistic model of trust arising differently in multiple parallel systems that perform distinct, complementary information processing. Because each system learns via different computational mechanisms, they will interact with the environment differently, and trust can be created (or destroyed) in multiple ways. This systems- level taxonomy of information representations provides a principled basis for differentiating forms of trust, linking them to specific learning processes, and generating testable predictions about their expression in behavior. Furthermore, because these different computational processes are implemented by different neural circuits, our theory makes testable predictions about the different neural circuits underlying different kinds of trust. By situating trust within a broader theory of neural decision systems, our account unifies diverse findings across psychology, neuroscience, and the social sciences, and offers a foundation for explaining how humans develop, maintain, lose, and repair trust in a complex social world.
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