Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning.

中文翻译：

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简化社交学习


社交学习很复杂，但人们似乎常常能够轻松驾驭社交环境。这种能力给强化学习（RL）的传统解释带来了难题，传统的强化学习假设人们在简单但简单的行为（无模型学习）和复杂但困难的行为（例如基于模型的学习）之间进行权衡。我们提供了一个解决这个难题的理论框架：尽管社会环境很复杂，但人们拥有的社会专业知识可以帮助他们以较低的认知成本灵活行事。具体来说，通过使用熟悉的概念而不是关注新的细节，人们可以将困难的学习问题变成更简单的问题。这种能力突出了社会学习作为研究面对环境复杂性时认知简单性的原型，并确定了概念知识在日常奖励学习中的作用。