Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in…

Young infants remember their prior experiences for relatively long periods with surprising specificity, and even seemingly forgotten memories can often be reactivated to further protract retention. These reactivations can be programmed in ways that optimize cumulative learning and retention, based on the principles embodied in the time-window…

Introduces the concepts of online and offline sampling and highlights the role of online sampling in agent-based models of learning and development. Compares the strengths of each approach for modeling particular developmental phenomena and research questions. Describes a recent agent-based model of infant causal perception. Discusses limitations…

This book contains a collection of papers that focus on normal infant development, particularly from the standpoint of learning. Written by leading experts from a member of disciplines, these papers deal with topics such as: the examination and observation of infants, including neurological, neuropsychological, and neurobehavioral aspects;…