How Features Create Knowledge of Kinds

Given a single instance of a novel category, twoand threeyear-old children systematically generalize its name to other novel things based on appropriate feature dimensions. We explain this in terms of a prediction of the probabilistic density (category likelihood) in feature space from a single novel instance. In principle, observing more instances from a particular probabilistic density, one can estimate the probabilistic density more accurately. In this sense, children’s success in generalization from a single instance seems to go beyond the theoretical limit. We provide a theoretical account for the phenomenon. In our theory, these kind of kind specific generalizations, a fast mapping from a single instance to a whole category is due to the structure of the system of learned categories and a sort of optimization of the category organization.