Computational modeling in developmental science

One of the central goals of scientific inquiry is to understand the mechanism underlying the observed phenomena. Scientific methods (such as experimental design, measurement theory, and statistics) help solving these problems. Like other methods, computational modeling, the subject of this chapter, offers a collection of tools that advance scientific understanding. We consider five case studies in which computational modeling used to examine issues in infant memory for specific and general information, attention and category learning, memory development, and decision making. We demonstrate how computational modeling may advance our understanding of developmental changes across these broad domains in developmental science. We discuss how modeling complements experimentation and how it allows researchers to test hypotheses about theoretical variables giving rise to various data patterns.