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.
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.
R
local common ground
our shared history
D
of convention
R
abstract away meanings that seem to be shared across partners
solve this inductive problem
D
across partners,
R
local adaptation as well as collective convergence
both partner-specificity and community convergence that distinguish our model from alternatives
R
local partner-specific learning
partner-specificity
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global convention formation
the formation of community-wide conventions,
D
hierarchical