| Abstract: | Recent neurobiological evidence suggests that environmentally derived activity plays a central role in regulating neuronal growth and neuronal connectivity. Artificial neural networks with distributed representations display many features of knowing and learning that are known from biological intelligence. In this article, I advocate artificial neural networks as models for cognition and development. These models and how they work are exemplified in the context of a well‐known Piagetian developmental task and school science activity: balance beam problems. I conclude that artificial neural networks, because of their profoundly interactivist nature, are ideal tools for modeling cognitive development and learning in science. © 2000 John Wiley & Sons, Inc. J Res Sci Teach 37: 63–80, 2000 |
