Abstract
We present an alternative model of human cognitive development on the balance scale task. Study of this task has inspired a wide range of human and computational work. The task requires that children predict the outcome of placing a discrete number of weights at various distances on either side of a fulcrum. Our 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 those of previous computational efforts.
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Schmidt, W.C., Ling, C.X. A decision-tree model of balance scale development. Mach Learn 24, 203–230 (1996). https://doi.org/10.1007/BF00058612
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DOI: https://doi.org/10.1007/BF00058612