The acquisition of new categories through grounded symbols: An extended connectionist model

Abstract

Solutions to the symbol grounding problem, in psychologically plausible cognitive models, have been based on hybrid connectionist/symbolic architectures, on robotic approaches and on connectionist only systems. This paper presents new simulations on the use of neural network architectures for the grounding of symbols on categories. In particular, the connectivity patterns between layers of the networks will be manipulated to scale up the performance of current connectionist models for the acquisition of higher-order categories via grounding transfer. © Springer-Verlag Berlin Heidelberg 2003.