Artificial neural networks for modeling knowing and learning in science

Recent neurobiological evidence suggests that environmentally derived activ ity plays a central role in regulating neuronal growth and neuronal connect ivity. Artificial neural networks with distributed representations display many features of knowing and learning that are known from biological intell igence. In this article, I advocate artificial neural networks as models fo r cognition and development. These models and how they work are exemplified in the context of a well-known Piagetian developmental task and school sci ence activity: balance beam problems. I conclude that artificial neural net works, because of their profoundly interactivist nature, are ideal tools fo r modeling cognitive development and learning in science. (C) 2000 John Wil ey & Sons, Inc.