Autoassociator networks: insights into infant cognition.
This paper presents autoassociator neural networks. A first section reviews the architecture of these models, common learning rules, and presents sample simulations to illustrate their abilities. In a second section, the ability of these models to account for learning phenomena such as habituation is reviewed. The contribution of these networks to discussions about infant cognition is highlighted. A new, modular approach is presented in a third section. In the discussion, a role for these learning models in a broader developmental framework is proposed.
Department of Psychology, University of Manchester, UK. sylvain.sirois@man.ac.uk
Sylvain
S
Sirois
D001185:Artificial Intelligence; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D008960:Models, Psychological; D016571:Neural Networks, Computer