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)

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 t...

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Bibliographic Details
Main Author: Fenna Poletiek
Format: Article
Language:English
Published: PsychOpen GOLD/ Leibniz Institute for Psychology 2011-06-01
Series:Biolinguistics
Subjects:
Online Access:https://doi.org/10.5964/bioling.8827
Description
Summary: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.
ISSN:1450-3417