Even once children can accurately remember their experiences, they nevertheless struggle to use those memories in flexible new ways--as in when drawing inferences. However, it remains an open question as to whether the developmental differences observed during both memory formation and inference itself represent a fundamental limitation on…

Understanding how learning changes during human development has been one of the long-standing objectives of developmental science. Recently, advances in computational biology have demonstrated that humans display a bias when learning to navigate novel environments through rewards and punishments: they learn more from outcomes that confirm their…

Infants, children and adults are capable of extracting recurring patterns from their environment through statistical learning (SL), an implicit learning mechanism that is considered to have an important role in language acquisition. Research over the past 20 years has shown that SL is present from very early infancy and found in a variety of tasks…

A core assumption of many theories of development is that children can learn indirectly from other people. However, indirect experience (or testimony) is not constrained to provide veridical information. As a result, if children are to capitalize on this source of knowledge, they must be able to infer who is trustworthy and who is not. How might a…

Piaget proposed that understanding permanency, understanding occlusion events, and forming mental representations were synonymous; however, accumulating evidence indicates that those concepts are "not" unified in development. Infants reach for endarkened objects at younger ages than for occluded objects, and infants' looking patterns suggest that…

Hebbian learning is a biologically plausible and ecologically valid learning mechanism. In Hebbian learning, "units that fire together, wire together". Such learning may occur at the neural level in terms of long-term potentiation (LTP) and long-term depression (LTD). Many features of Hebbian learning are relevant to developmental theorizing,…

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…