A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an A(n)B(n) grammar draw conflicting conclusions (Bahlmann & Friederici, 2006; De Vries, Monaghan, Knecht, & Zwitserlood, 2008). We argue that 2 conditions crucially affect learning A(n)B(n) 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.
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