A Decision-Tree Model of Balance Scale Development
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.
Cite
Text
Schmidt and Ling. "A Decision-Tree Model of Balance Scale Development." Machine Learning, 1996. doi:10.1007/BF00058612Markdown
[Schmidt and Ling. "A Decision-Tree Model of Balance Scale Development." Machine Learning, 1996.](https://mlanthology.org/mlj/1996/schmidt1996mlj-decisiontree/) doi:10.1007/BF00058612BibTeX
@article{schmidt1996mlj-decisiontree,
title = {{A Decision-Tree Model of Balance Scale Development}},
author = {Schmidt, William C. and Ling, Charles X.},
journal = {Machine Learning},
year = {1996},
pages = {203-230},
doi = {10.1007/BF00058612},
volume = {24},
url = {https://mlanthology.org/mlj/1996/schmidt1996mlj-decisiontree/}
}