A constructive neural-network approach to modeling psychological development

This article reviews a particular computational modeling approach to the study of psychological development – that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept learning, and theory of mind. Implications of this modeling for theoretical understanding of psychological development are considered.

► Constructive neural network models of psychological development are reviewed.
► Such models construct their own topology, and build new learning on top of existing knowledge.
► With few changes, they have been successfully applied to a diverse range of empirical findings.
► Implications for psychological theories of development are discussed.