Abstract
Conventional research methods for investigating development are powerful and diverse, but they also have their limits. Many of these limitations can be overcome or addressed through computer modeling. To help make this argument, the current chapter provides a broad, accessible overview to the study of computational models of learning and development. First, we explore the technical vocabulary of computational modeling research by reviewing a set of basic concepts, including the different kinds of representations that are employed by computational models, as well as the array of learning algorithms that are typically used. Next, we review four major types of models: connectionist models, dynamic field theory models, rule-based models, and Bayesian models. In the final section, we put these concepts and approaches into practice by surveying findings from models that simulate the development of object knowledge, language learning, and motor-skill acquisition.
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