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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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
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author Fenna Poletiek
author_facet Fenna Poletiek
author_sort Fenna Poletiek
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description 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.
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spelling doaj.art-ee52a47f94064946a3c976e6e5d5fa172024-01-31T10:02:57ZengPsychOpen GOLD/ Leibniz Institute for PsychologyBiolinguistics1450-34172011-06-0151-203604210.5964/bioling.88278827What 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 Poletiek0Leiden UniversityIn 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.https://doi.org/10.5964/bioling.8827artificial grammar learninglinguistic environmentrecursionstaged inputstatistical learning
spellingShingle Fenna Poletiek
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)
Biolinguistics
artificial grammar learning
linguistic environment
recursion
staged input
statistical learning
title 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)
title_full 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)
title_fullStr 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)
title_full_unstemmed 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)
title_short 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)
title_sort 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
topic artificial grammar learning
linguistic environment
recursion
staged input
statistical learning
url https://doi.org/10.5964/bioling.8827
work_keys_str_mv AT fennapoletiek whatintheworldmakesrecursionsoeasytolearnastatisticalaccountofthestagedinputeffectonlearningacenterembeddedstructureinartificialgrammarlearningagl