When correlation equals causation: A behavioral and computational account of second-order correlation learning in children

Abstract

We examined 2- and 3-year-old children’s ability to use second-order correlation learning—in which a learned correlation between two pairs of features (e.g., A and B, A and C) is generalized to the noncontiguous features (i.e., B and C)—to make causal inferences. Previous findings showed that 20- and 26-month-old children can use second-order correlation learning to learn about static and dynamic features in category and noncategory contexts. The current behavioral study and computational model extend these findings to show that 2- and 3-year-olds can detect the second-order correlation between an object’s surface feature and its capacity to activate a novel machine, but only if the children had encoded the first-order correlations on which the second-order correlation was based. These results have implications for children’s developing information-processing capacities on their ability to use second-order correlations to infer causal relations in the world.