将意义从伙伴推广到群体：层次推理支持网络上的约定形成
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks
摘要：
语言惯例的一个关键属性是，它们覆盖了整个说话者群体，使我们能够有效地与我们从未见过的人进行交流。与此同时，我们的语言使用在很大程度上是针对特定的伙伴的：我们知道，基于我们共同的历史，不同的人可能会对单词有不同的理解。这对关于惯例形成的叙述提出了挑战。代理人究竟是如何在保持合作伙伴特定知识的同时，实现整个社区期望的推理飞跃的？我们提出了一个分层贝叶斯模型来解释如何扬声器和听众解决这个归纳问题。为了评估我们的模型的预测，我们进行了一项实验，参与者在一个小社区中与不同的合作伙伴玩扩展的自然语言交流游戏。我们研究了几种泛化措施，并找到了区分我们的模型与替代方案的合作伙伴特异性和社区融合的关键签名。这些结果表明，合作伙伴的特异性不仅是兼容的社区范围内的公约的形成，但可能会促进它时，加上一个强大的诱导机制。
英文摘要：
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 our shared history. 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 to explain how speakers and listeners solve this inductive problem. 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 and find key signatures of both partner-specificity and community convergence that distinguish our model from alternatives. These results suggest that partner-specificity is not only compatible with the formation of community-wide conventions, but may facilitate it when coupled with a powerful inductive mechanism.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
