Inter- and intra-dimensional dependencies in implicit phonotactic learning

Abstract: Is phonological learning subject to the same inductive biases as learning in other domains? Previous studies of non-linguistic learning found that intra-dimensional dependencies (between two instances of the same feature) were learned more easily than inter-dimensional ones. This study com...

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Published in:Journal of Memory & Language pp. 165 - 184
Main Author: Moreton, Elliott
Format: Article
Published: Elsevier B.V. Jul2012
Subjects:
Online Access:View this record in EBSCOhost
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      dt: Jul2012
      pid: 467
      pub: Elsevier B.V.
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        76609859
        10.1016/j.jml.2011.12.003
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        atl: Inter- and intra-dimensional dependencies in implicit phonotactic learning
      aug:
        au: Moreton, Elliott
        affil: Department of Linguistics, University of North Carolina, Chapel Hill, NC 27599-3155, USA
      su:
        Learning
        Phonetics
        Linguistics
        Phonotactics
        Inductive interference
        Statistical correlation
      sug:
        subj:
          Learning
          Phonetics
          Linguistics
          Phonotactics
          Inductive interference
          Statistical correlation
      keyword:
        Complexity
        Concept learning
        Implicit learning
        Inductive bias
        Phonotactic learning
        Complexity
        Concept learning
        Implicit learning
        Inductive bias
        Phonotactic learning
      ab: Abstract: Is phonological learning subject to the same inductive biases as learning in other domains? Previous studies of non-linguistic learning found that intra-dimensional dependencies (between two instances of the same feature) were learned more easily than inter-dimensional ones. This study compares implicit learning of intra- and inter-dimensional phonotactic dependencies. A series of six unsupervised implicit-learning experiments shows that a pattern based on agreement between two instances of the same feature is easier to learn than one based on correlation between instances of two different features. The results are interpreted as evidence for domain-general restrictions on the form of domain-specific learning primitives.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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