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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-olds can use second-order correlation learning to learn about static and dynamic features in category and non-category contexts. The present behavioral study and computational model extend these findings to show that 2-and 3-year-old children 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.

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