Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies

Abstract

When children learn their native language, they have to deal with a confusing array of dependencies between various elements in an utterance. The dependent elements may be adjacent to one another or separated by intervening material. Prior studies suggest that nonadjacent dependencies are hard to learn when the intervening material has little variability, which may be due to a trade‐off between adjacent and nonadjacent learning. In this study, we investigate the statistical learning of adjacent and nonadjacent dependencies under low intervening variability using a modified serial reaction time (SRT) task. Young adults were trained on mixed sets of materials comprising equally probable adjacent and nonadjacent dependencies. Offline tests administered after training showed better performance for adjacent than nonadjacent dependencies. However, online SRT data indicated that the participants developed sensitivity to both types of dependencies during training, with no significant differences between dependency types. The results demonstrate the value of online measures of learning and suggest that adjacent and nonadjacent learning can occur together even when there is low variability in the intervening material.