Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks

TL;DR

This paper introduces a hierarchical Bayesian model explaining how linguistic conventions form across communities while accommodating partner-specific meanings, supported by experimental evidence showing both partner-specificity and community convergence.

Contribution

The paper presents a novel hierarchical Bayesian model that accounts for how individuals generalize language meanings from partners to entire communities, supported by experimental validation.

Findings

Model captures partner-specific and community-wide language generalization

Experimental results show signatures of both partner-specificity and convergence

Partner-specificity can facilitate community convention formation

Abstract

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.