Study title

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 information selection that maximizes learning. We first present a neurocomputational model of infant visual category learning, capturing existing empirical data on the role of environmental complexity on learning. Next we “set the model free”, allowing it to select its own stimuli based on a formalization of curiosity and three alternative selection mechanisms. We demonstrate that maximal learning emerges when the model is able to maximize stimulus novelty relative to its internal states, depending on the interaction across learning between the structure of the environment and the plasticity in the learner itself. We discuss the implications of this new curiosity mechanism for both existing computational models of reinforcement learning and for our understanding of this fundamental mechanism in early development.The overall goal of this award was to understand how babies learn when allowed to explore their environment based on their own curiosity, outside the constrained experimental setting typical of most research in early cognitive development. We were also interested in how this curiosity-based exploration might be influenced by language. This goal was approached in two ways: first using computational modelling to examine the potential learning mechanisms involved in curiosity; and second, experimentally, to develop a picture of what babies and toddlers do when engaged in curiosity-driven learning. In our computational work we developed the first model of babies curiosity-driven learning inspired by the mechanisms known to exist in the human brain. This model predicted that when allowed to freely choose what to learn from and...