Development of children's seriation: A connectionist approach

This paper presents a modular connectionist network model of the developmen t of seriation (sorting) in children. The model uses the cascade-correlatio n generative connectionist algorithm. These cascade-correlation networks do better than existing rule-based models at developing through soft stage tr ansitions, sorting more correctly with larger stimulus size increments and showing variation in seriation performance within stages. However, the full generative power of cascade-correlation was not found to be a necessary co mponent for successfully modelling the development of seriation abilities. Analysis of network weights indicates that improvements in seriation are du e to continuous small changes instead of the radical restructuring suggeste d by Piaget. The model suggests that seriation skills are present early in development and increase in precision during later development. The require d learning environment has a bias towards smaller and nearly ordered arrays . The variability characteristic of children's performance arises from sort ing subsets of the total array. The model predicts better sorting moves wit h more array disorder, and a dissociation between which element should be m oved and where it should be moved.