Second-order correlation learning in 2- to 4-year-old children, and its underlying mechanism

Second-order correlation learning-or the capacity to infer an indirect relation between features based on separate direct relations-has been studied extensively in children older than 4 years of age. Comparatively less research has examined whether children 4 years of age and younger can engage in this process. One of the few studies that assessed second-order correlation learning in children younger than this age showed that 2- to 3-year-olds can use this form of learning to make causal inferences in a discrimination context (e.g., Benton et al., 2021). The present study extended this work in two key ways. First, it tested whether 2- to 4-year-olds could detect and encode second-order correlations that are embedded in a cognitively more demanding category-like context consisting of multiple objects and then use those correlations to make causal inferences. Second, it explored a possible mechanistic basis of second-order correlation learning through a connectionist computational model. The results of the behavioral study revealed that children can detect second-order correlations among several distinct objects and use them to make causal inferences, and the computational model captured children's performance and elucidated a possible mechanism through which such correlations might emerge in the mind. Taken together, the results demonstrate that children as young as two can use second-order correlations to guide causal inference in more complex contexts and that this ability may emerge from more general processes of the mind based on representational overlap.