Top-Down Grouping Affects Adjacent Dependency Learning

A large body of research has demonstrated that humans attend to adjacent co-occurrence statistics when processing sequential information, and bottom-up prosodic information can influence learning. In this study, we investigated how top-down grouping cues can influence statistical learning. Specifically, we presented English sentences that were structurally equivalent to each other, which induced top-down expectations of grouping in the artificial language sequences that immediately followed. We show that adjacent dependencies in the artificial language are learnable when these entrained boundaries bracket the adjacent dependencies into the same sub-sequence, but are not learnable when the elements cross an induced boundary, even though that boundary is not present in the bottom-up sensory input. We argue that when there is top-down bracketing information in the learning sequence, statistical learning takes place for elements bracketed within sub-sequences rather than all the elements in the continuous sequence. This limits the amount of linguistic computations that need to be performed, providing a domain over which statistical learning can operate