Symbol grounding transfer with hybrid self-organizing/supervised neural networks

This paper reports new simulations on an extended neural network model for the transfer of symbol grounding. It uses a hybrid and modular connectionist model, consisting of an unsupervised, self-organizing map for stimulus classification and a supervised network for category acquisition and naming. The model is based on a psychologically-plausible view of symbolic communication, where unsupervised concept formation precedes the supervised acquisition of category names. The simulation results demonstrate that grounding is transferred from symbols denoting object properties to newly acquired symbols denoting the object as a whole. The implications for cognitive models integrating neural networks and multi-agent systems are discussed.