This chapter provides a comparative survey of computational models of psychological development. To understand how computational modeling can contribute to the study of psychological development, it is important to appreciate the enduring issues in developmental psychology. The most common computational techniques applied to psychological development are production systems, connectionist networks, dynamic systems, robotics, and Bayesian inference. The chapter discusses modeling in the areas of the balance scale, past tense, object permanence, artificial syntax, similarity-to-correlation shifts in category learning, discrimination-shift learning, concept and word learning, and abnormal development. Some of the models reviewed in this chapter simulated development with programmer designed parameter changes. Variations in such parameter settings were used to implement age-related changes in both connectionist and dynamic-systems models of the A-not-B error, the Cascade-Correlation (CC) model of discrimination-shift learning, all three models of the similarity-to-correlation shift, and the autism model.