| Abstract: | We present two habituation experiments that examined 20‐ and 26‐month‐olds’ ability to engage in second‐order correlation learning for static and dynamic features, whereby learned associations between two pairs of features (e.g., P and Q, P and R) are generalized to the features that were not presented together (e.g., Q and R). We also present results from an associative learning mechanism that was implemented as an autoencoder parallel distributed processing (PDP) network in which second‐order correlation learning is shown to be an emergent property of the dynamics of the network. The experiments and simulation demonstrate that 20‐ and 26‐month‐olds as well as neural networks are capable of second‐order correlation learning in a category context for internal features of dynamic objects. However, the model predicts—and Experiment 3 demonstrates—that 20‐ and 26‐month‐olds are unable to encode second‐order correlations in a noncategory context for dynamic objects with internal features. It is proposed that the ability to learn second‐order correlations represents a powerful but as yet unexplored process for generalization in the first years of life. |
