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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. 相似文献
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