The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures

A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an AⁿBⁿ grammar draw conflicting conclusions (Bahlmann and Friederici, 2006 and De Vries et al., 2008] ). We argue that 2 conditions crucially affect learning AⁿBⁿ structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input. In 2 AGL experiments, learning was observed only when the training set was staged and contained 0-LoE exemplars. Our results might help understanding how natural complex structures are learned from exemplars.