An Investigation of Balance Scale Success

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 runs. We 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.