Fedforward neural network models of cognitive development are reviewed within the framework of a functional distinction between learning and development. This analysis suggests that static architecture networks implement a learning theory, whereas generative architecture networks combine learning and development. Both types of networks are then evaluated m terms of genetic costs. Within a levels-of-innateness framework, generative architectures are viewed as more plausible than static ones. Static architecture networks appear to implement a form of nativistic elicitation.

The importance of discrimination shifts to learning and developmental psychology is highlighted. Basic tasks used in continuous and total change paradigms are presented, and major theoretical accounts are briefly reviewed. The lack of a general and comprehensive interpretation of human shift learning is identified, and a recent model based on neural network research is described. This model suggests that human adult performance in discrimination shifts differs from preschool performance because of a process called spontaneous overtraining. This hypothesis has been previously used in neural network simulations to successfully capture developmental regularities in continuous discrimination shifts (e.g., reversal and nonreversal shifts). In the present paper, new simulations using this model are applied to total change discrimination shifts (e.g., intradimensional and extradimensional shifts). Several developmental regularities are successfully captured by the networks. The contribution of the spontaneous overtraining hypothesis is discussed.