Generalizing meanings from partners to populations:Hierarchical inference supports convention formation on networks
Abstract
A key property of linguistic conventions is that they hold overan entire community of speakers, allowing us to communicateefficiently even with people we have never met before. Atthe same time, much of our language use is partner-specific:we know that words may be understood differently by differ-ent people based on our shared history. This poses a chal-lenge for accounts of convention formation. Exactly how doagents make the inferential leap to community-wide expecta-tions while maintaining partner-specific knowledge? We pro-pose a hierarchical Bayesian model to explain how speakersand listeners solve this inductive problem. To evaluate ourmodel’s predictions, we conducted an experiment where par-ticipants played an extended natural-language communicationgame with different partners in a small community. We ex-amine several measures of generalization and find key signa-tures of both partner-specificity and community convergencethat distinguish our model from alternatives. These resultssuggest that partner-specificity is not only compatible with theformation of community-wide conventions, but may facilitateit when coupled with a powerful inductive mechanism.