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Artificial Neural Networks for Modeling Knowing and Learning in Science

✍ Scribed by Wolff-Michael Roth

Publisher
John Wiley and Sons
Year
2000
Tongue
English
Weight
94 KB
Volume
37
Category
Article
ISSN
0022-4308

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✦ Synopsis

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.

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