Language is a symbolic, culturally transmitted system of communication, which is learnt through the inference of meaning. In this paper, I describe the importance of meaning inference, not only in language acquisition, but also in developing a unified explanation for language change and evolution. Using an agent-based computational model of meaning creation and communication, I show how the meanings of words can be inferred through disambiguation across multiple contexts, using cross-situational statistical learning. I demonstrate that the uncertainty inherent in the process of meaning inference, moreover, leads to stable variation in both conceptual and lexical structure, providing evidence which helps to explain how language changes rapidly without losing communicability. Finally, I describe how an inferential model of communication may provide important theoretical insights into plausible explanations of the bootstrapping of, and the subsequent progressive complexification of, cultural communication systems.
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