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One of the key unsolved problems in cognitive development is the precise specification of developmental transition mechanisms. As the work in this volume attests, it is clear that computational modeling can provide insights into this problem. In this chapter, we focus on the applicability of a specific generative connectionist algorithm, cascade-correlation (Fahlman & Lebiere, 1990), as a process model of transition mechanisms. Generative connectionist algorithms build their own network topologies as they learn, allowing them to simulate both qualitative and quantitative developmental changes. We compare and contrast cascade-correlation, Piaget's notions of assimilation and accommodation, Papert's little known but historically relevant genetron model, conventional back-propagation networks, and rule-based models.

Specific cascade-correlation models of a wide range of developmental phenomena are presented. These include the balance scale task; concepts of potency and resistance in causal reasoning; seriation; integration of the concepts of distance, time, and velocity; and personal pronouns. Descriptions of these simulations stress the degree to which the models capture the essential known psychological phenomena, generate new testable predictions, and provide explanatory insights. In several cases, the simulation results underscore clear advantages of connectionist modeling techniques. Abstraction across the various models yields a set of domain-general constraints for cognitive development. Particular domain-specific constraints are identified. Finally, the models demonstrate that connectionist approaches can be successful even on relatively high-level cognitive tasks. 


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