What in the World Makes Recursion so Easy to Learn? A Statistical Account of the Staged Input Effect on Learning a Center-Embedded Structure in Artificial Grammar Learning (AGL)

Fenna Poletiek


In an artificial grammar learning study, Lai & Poletiek (2011) found that human participants could learn a center-embedded recursive grammar only if the input during training was presented in a staged fashion. Previous studies on artificial grammar learning, with randomly ordered input, failed to demonstrate learning of such a center-embedded structure. In the account proposed here, the staged input effect is explained by a fine-tuned match between the statistical characteristics of the incrementally organized input and the development of human cognitive learning over time, from low level, linear associative, to hierarchical processing of long distance dependencies. Interestingly, staged input seems to be effective only for learning hierarchical structures, and unhelpful for learning linear grammars.


artificial grammar learning; linguistic environment; recursion; staged input; statistical learning

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