title: The learning barrier: Moving from innate to learned systems of communication
creator: Oliphant, Michael
subject: Animal Behavior
subject: Animal Cognition
subject: Evolution
subject: Language
subject: Dynamical Systems
subject: Neural Nets
subject: Computational Linguistics
subject: Learnability
subject: Semantics
subject: Philosophy of Language
description: 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.
date: 1988-04
type: Preprint
type: NonPeerReviewed
format: application/postscript
identifier: http://cogprints.org/196/2/netlearn.ps
identifier:   Oliphant, Michael  (1988) The learning barrier: Moving from innate to learned systems of communication.  [Preprint]    (Unpublished)  
relation: http://cogprints.org/196/