We propose a society-first theory of normative appropriateness where individuals, modeled as pre-trained actors with cognitive architectures analogous to Large Language Models (LLMs), generate behavior via predictive pattern completion. Our theory posits that individuals act by completing distributed symbolic patterns based on context, answering questions such as "What does a person such as I do in a situation such as this?". This sense-making mechanism provides a parsimonious account of the key features of human norms: their context-dependence, arbitrariness, automaticity, dynamism, and their support from social sanctioning. It challenges rational-choice theories of social norms by accounting for their key features without needing to exogenously posit scalar rewards or preference relations. By distinguishing between explicit norms, which we associate with in-context adaptation, and implicit norms, which we associate with long-term memory, the theory reconceptualizes several foundational ideas in cognitive science. In particular, it gives an alternative account to the data traditionally seen as supporting dual-process models, and it flips the role of rationality, allowing us to construe it as adherence to culturally-contingent justification standards.

翻译：我们提出了一种社会优先的规范性适当性理论，其中个体被建模为具有类似于大型语言模型（LLMs）认知架构的预训练行动者，通过预测性模式补全来生成行为。我们的理论认为，个体通过基于上下文补全分布式符号模式来行动，回答诸如"像我这样的人在此类情境中会做什么？"这样的问题。这种意义建构机制为人类规范的关键特征提供了简约的解释：其情境依赖性、任意性、自动性、动态性以及社会制裁的支持。该理论通过解释规范的关键特征而无需外在地设定标量奖励或偏好关系，从而挑战了关于社会规范的理性选择理论。通过区分显性规范（我们将其与情境内适应相关联）和隐性规范（我们将其与长期记忆相关联），该理论重新阐释了认知科学中的若干基础性概念。特别是，它为传统上被视为支持双过程模型的数据提供了另一种解释，并翻转了理性的角色，使我们能够将其理解为对文化偶然性辩护标准的遵循。