Proceedings of the Annual Meeting of the Cognitive Science Society

Natural languages exhibit properties that are difficult to explainfrom a purely functional perspective. One of these properties isthe systematic lack of upper-bounds in the literal meaning ofscalar expressions. This investigation addresses the develop-ment and selection of such semantics from a space of possiblealternatives. To do so we put forward a model that integratesBayesian learning into the replicator-mutator dynamics com-monly used in evolutionary game theory. We argue this syn-thesis to provide a suitable and general model to analyze thedynamics involved in the use and transmission of language.Our results shed light on the semantics-pragmatics divide andshow how a learning bias in tandem with functional pressuremay prevent the lexicalization of pragmatic inferences.