Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated…

The first 1,000 days of life are a critical window of vulnerability to exposure to socioeconomic and health challenges (i.e. poverty/undernutrition). The Brain Imaging for Global Health (BRIGHT) project has been established to deliver longitudinal measures of brain development from 0 to 24 months in UK and Gambian infants and to assess the impact…

That competences may emerge given appropriate environmental and behavioral context is a long-standing theme in developmental research. Work in the motor domain, but also in cognitive development, has made it possible to transform this idea into a mechanistic account closely linked to empirical evidence. In dynamic systems thinking, such capacities…

The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of experimental design and the causal Bayes net formalism. Two studies suggest that preschoolers can use the conditional intervention principle to distinguish causal chains, common cause…

Three experiments examined children's knowledge of harmony in Western music. The children heard a series of chords followed by a final, target chord. In Experiment 1, French 6- and 11-year-olds judged whether the target was sung with the vowel /i/ or /u/. In Experiment 2, Australian 8- and 11-year-olds judged whether the target was played on a…

Neural networks are applied to a theoretical subject in developmental psychology: modeling developmental transitions. Two issues that are involved will be discussed: discontinuities and acquiring qualitatively new knowledge. We will argue that by the appearance of a bifurcation, a neural network can show discontinuities and may acquire…

This paper presents autoassociator neural networks. A first section reviews the architecture of these models, common learning rules, and presents sample simulations to illustrate their abilities. In a second section, the ability of these models to account for learning phenomena such as habituation is reviewed. The contribution of these networks to…