The ability to combine linguistic guidance from others with direct experience is central to human development, enabling safe and rapid learning in new environments. How do people integrate these two sources of knowledge, and how might AI systems? We present a computational framework that models social learning as joint probabilistic inference over structured, executable world models given sensorimotor and linguistic data. We make this possible by turning a pretrained language model into a probabilistic model of how humans share advice conditioned on their beliefs, allowing our agents both to generate advice for others and to interpret linguistic input as evidence during Bayesian inference. Using behavioral experiments and simulations across 10 video games, we show how linguistic guidance can shape exploration and accelerate learning by reducing risky interactions and speeding up key discoveries in both humans and models. We further explore how knowledge can accumulate across generations through iterated learning experiments and demonstrate successful knowledge transfer between humans and models -- revealing how structured, language-compatible representations might enable human-machine collaborative learning.

翻译：将他人语言指导与直接经验相结合的能力是人类发展的核心，它使得在新环境中能够安全、快速地进行学习。人们如何整合这两种知识来源，人工智能系统又该如何实现？我们提出了一个计算框架，该框架将社会学习建模为给定感觉运动与语言数据时，对结构化、可执行世界模型的联合概率推断。我们通过将预训练语言模型转化为一个基于人类信念分享建议的概率模型来实现这一点，使我们的智能体既能为他人生成建议，又能在贝叶斯推断过程中将语言输入解释为证据。通过在10款视频游戏中进行行为实验与仿真，我们展示了语言指导如何通过减少危险交互并加速关键发现的进程，来塑造探索行为并促进学习——这一规律在人类与模型中均得到验证。我们进一步通过迭代学习实验探索了知识如何跨代积累，并展示了人类与模型之间成功的知识迁移——揭示了结构化、与语言兼容的表征如何可能实现人机协作学习。