The learning barrier: Moving front innate to learned systems of communication

Human language is a unique ability. It sits apart from other systems of com munication in two striking ways: it is syntactic, and it is learned. While most approaches to the evolution of language have focused on the evolution of syntax, this paper explores the computational issues that arise in shift ing from a simple innate communication system to an equally simple one that is learned. Associative network learning within an observational learning paradigm is used to explore the computational difficulties involved in esta blishing and maintaining a simple learned communication system. Because Heb bian learning is found to be sufficient for this task, it is proposed that the basic computational demands of learning are unlikely to account for the rarity of even simple learned communication systems. Instead, it is the pr oblem of *observing* that is likely to be central - in particular the probl em of determining what meaning a signal is intended to convey.