title: Modeling the evolution of communication: From stimulus associations to grounded symbolic associations
creator: Cangelosi, Angelo
subject: Artificial Intelligence
subject: Neural Nets
subject: Evolutionary Psychology
subject: Computational Linguistics
subject: Psychophysics
description: This paper describes a model for the evolution of communication systems using simple syntactic rules, such as word combinations. It also focuses on the distinction between simple word-object associations and symbolic relationships. The simulation method combines the use of neural networks and genetic algorithms. The behavioral task is influenced by Savage-Rumbaugh & Rumbaugh’s (1978) ape language experiments. The results show that languages that use combination of words (e.g. “verb-object” rule) can emerge by auto-organization and cultural transmission. Neural networks are tested to see if evolved languages are based on symbol acquisition. The implications of this model for Deacon’s (1997) hypothesis on the role of symbolic acquisition for the origin of language are discussed.
publisher: Speinger Verlag
contributor: Floreano, D
contributor: Nicoud, J
contributor: Mondada, F
date: 1999
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/2022/3/cangelosi-ecal99.pdf
identifier:   Cangelosi, Angelo  (1999) Modeling the evolution of communication: From stimulus associations to grounded symbolic associations.  [Conference Paper]     
relation: http://cogprints.org/2022/