Within the past decade a number of symbolic and connectionist learning methods have been applied to cognitive development's balance scale task. The aim of this body of research has been to investigate the use of machine learning methods as models of developmental transition, to explore the range of assumptions under which psychologically accurate models of the task can be achieved, and most important, to assemble predictions about the task and the changes that children's thinking undergoes during the course of development. Study of this task has inspired a wide range of human and computational work that will be reviewed in this talk. The task requires that children predict the outcome of placing a discrete number of weights at various distances on either side of a fulcrum. A recent model which features the symbolic learning algorithm C4.5 as a transition mechanism, exhibits regularities found in the human data including orderly stage progression, U-shaped development, and the torque difference effect. Unlike previous successful models of the task, the current model uses a single free parameter, is not restricted in the size of the balance scale that it can accommodate and does not require the assumption of a highly structured output representation or a training environment biased towards weight or distance information. The model makes a number of predictions differing from previous computational efforts.