Proceedings of the Annual Meeting of the Cognitive Science Society

The success of a connectionist model of cognitive development on the balance scale task is due to manipulations which impede convergence of the backpropagation learning algorithm. The model was trained at different levels of a biased training environment with exposure to a varied number of training instances. The effects of weight updating method and modifying the network topology were also examined. In all cases in which these manipulations caused a decrease in convergence rate, there was an increase in the proportion of psychologically realistic nms. W e conclude that incremental connectionist learning is not sufficient for producing psychologically successful connectionist balance scale models, but must be accompanied by a slowing of convergence.