Shultz, T. R. (2001). Connectionist models of development. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social and Behavioral Science (Vol. 4, pp. 2577-2580). Oxford: Pergamon.



This article begins with a brief characterization of connectionism, a style of computation based on principles of brain functioning and the mathematics of statistical mechanics. Connectionist networks are considered useful for modeling psychological development because of their graded knowledge representations, capacity for change and self-organization, ability to implement environment-heredity interactions, and neurological plausibility. Connectionist techniques used to model development include supervised and unsupervised learning, hidden-unit recruitment, and auto-association. The article concentrates on how connectionist models have contributed to the understanding of some important issues in psychological development: cognitive stages and perceptual effects, transition mechanisms, non-normative stages, developmental lags, modularity, self-organization, integration of diverse findings, explanation of mysterious effects, and resolution of theoretical disputes. Connectionist approaches provide a novel view of how knowledge is represented in children and a compelling picture of how and why developmental transitions occur. Such models can also cover aspects of social and language development in children. Future research will likely address some desirable features lacking in current connectionist models.


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