A key property of linguistic conventions is that they hold over an entire community of speakers, allowing us to communicate efficiently even with people we have never met before. At the same time, much of our language use is partner-specific: we know that words may be understood differently by different people based on local common ground. This poses a challenge for accounts of convention formation. Exactly how do agents make the inferential leap to community-wide expectations while maintaining partner-specific knowledge? We propose a hierarchical Bayesian model of convention to explain how speakers and listeners abstract away meanings that seem to be shared across partners. To evaluate our model's predictions, we conducted an experiment where participants played an extended natural-language communication game with different partners in a small community. We examine several measures of generalization across partners, and find key signatures of local adaptation as well as collective convergence. These results suggest that local partner-specific learning is not only compatible with global convention formation but may facilitate it when coupled with a powerful hierarchical inductive mechanism.

探究语言约定如何形成整个社区期望结合个体专属知识，提出了基于分层贝叶斯模型的解决方案，并进行了针对具体伙伴的自然语言沟通实验进行了模型预测的评估，结果表明伙伴特异性不仅与社区范围内的约定形成相容，而且可以与强大的归纳机制相结合，加以促进。

从合作伙伴到人群的意义泛化：层次推断支持网络上的惯例形成