Computational models are powerful tools--too powerful, according to some. We argue that the idea that models can "do anything" is wrong, and we describe how their failures have been informative. We present new work showing surprising diversity in the effects of feedback on children's task-switching, such that some children perseverate despite this…

We examine the contributions of dynamic systems theory to the field of cognitive development, focusing on modeling using dynamic neural fields. After introducing central concepts of dynamic field theory (DFT), we probe empirical predictions and findings around two examples--the DFT of infant perseverative reaching that explains Piaget's A-not-B…

The present study illustrates the usefulness of finite mixture of generalized linear models (GLMs) to examine variability in cognitive strategies during childhood. More precisely, it addresses this variability in set-shifting situations where task-goal updating is endogenously driven. In a task-switching paradigm 5-6-year-olds had to switch…

This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…