Recent experimental and computational modelling work has found that languages are shaped by the referential context in which they operate. Wray & Grace (2007) argue that even compositionality, traditionally regarded as a universal and fun- damental feature of human languages, may have only cultur- ally evolved in response to changing social contexts. But how can the referential contexts of individual interactions come to shape the level of compositionality in the language of an entire community? To explore this question, we propose an iterated hierarchical Bayesian model that shows how partner-specific linguistic innovations can be generalized as community-wide features via a context-sensitive pathway. Our simulations show that the degree of compositionality that evolves in the language of a community depends on the communicative needs of its members, but also on the degree of user uncertainty over the nature of those needs, and on the level of heterogeneity in the community’s needs.
It has been argued that patterns of cross-linguistic variation in the semantic categories labelled by individual words are a result of a trade-off between cognitive pressures (so as to be simple to learn and use) and communicative pressures (so as to be efficient in communication). However, the question of what exact mechanisms control this trade-off has been left largely unanswered. We argue that one factor could be the extent to which referential contexts at the level of local interactions are similar or different across users of a category system. To test this hypothesis we propose a hierarchical Bayesian model for communication in a multidimensional meaning space, in which agents actively consider spatial similarity relations during interaction. Our models predict that less variability in contexts across interactions induces categories with lower communicative cost, while more variable contexts across partners are more strongly associated with category systems with lower cognitive cost.
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