GENERATIVE CONNECTIONIST NETWORKS AND CONSTRUCTIVIST COGNITIVE-DEVELOPMENT

This article presents a novel computational framework for modeling cog nitive development. The new modeling paradigm provides a language with which to compare and contrast radically different facets of children' s knowledge. Concepts from the study of machine learning are used to e xplore the power of connectionist networks that construct their own ar chitectures during learning. These so-called generative algorithms are shown to escape from Fodor's (1980) critique of constructivist develo pment We describe one generative connectionist algorithm (cascade-corr elation) in detail. We report on the successful use of the algorithm t o model cognitive development on balance scale phenomena; seriation; t he integration of velocity, time, and distance cues; prediction of eff ect sizes from magnitudes of causal potencies and effect resistances; and the acquisition of English personal pronouns. The article demonstr ates that computer models are invaluable for illuminating otherwise ob scure discussions.