TY - UNPB ID - cogprints196 UR - http://cogprints.org/196/ A1 - Oliphant, Michael TI - The learning barrier: Moving from innate to learned systems of communication Y1 - 1988/04// N2 - Human language is a unique ability. It sits apart from other systems of communication 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 shifting 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 establishing and maintaining a simple learned communication system. Because Hebbian 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 problem of *observing* that is likely to be central -- in particular the problem of determining what meaning a signal is intended to convey. AV - public KW - language KW - communication KW - evolution KW - evolution of language KW - learning KW - language learnability KW - meaning KW - semantics KW - computation KW - hebbian learning KW - network learning KW - observational learning KW - associative learning KW - dynamical systems ER -