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Article published In:
Structural similarity across domains in third language acquisition
Edited by Nadine Kolb, Natalia Mitrofanova and Marit Westergaard
[Linguistic Approaches to Bilingualism 13:5] 2023
► pp. 614–637

Using a contrastive hierarchy to formalize structural similarity as I-proximity in L3 phonology

ORCID logo | University of Victoria

In this paper I argue that cross-linguistic similarity in third language acquisition is determined by a structural hierarchy of contrastive phonological features. Such an approach allows us formalize a predictive notion of I-proximity which also provides an explanatory model of L2, and L3 phonological knowledge (represented in an integrated I-grammar). The metrics of phonological similarity (i.e., structural not acoustic) are analogous to morphosyntactic similarity in that both morphosyntactic and phonological approaches can compare the outcomes of parsing the L3 input by the L1 hierarchy and by the L2 hierarchy. From this starting point I propose a conservative, incremental learning theory to guide subsequent reconstruction of the L3 grammar. Under this model, it can be argued that phonology is part of Faculty of Language Narrow (FLN). The (gradient) phonetic material comes from outside the FLN but the linguistic computational system converts it to discrete abstract elements that can be manipulated by the learner.

Keywords: L3 phonology, I-proximity, Linguistic Proximity Model (LPM), contrastive hierarchy, cross-linguistic similarity
Article outline
  • 1.Introduction
  • 2.Invoking similarity
    • 2.1Measures of similarity
  • 3.Phonological machinery
    • 3.1The contrastive hierarchy
    • 3.2Contrastive hierarchy theory and optimality theory
  • 4.Multilingual acquisition
    • 4.1Phonological similarity
    • 4.2Previous approaches
    • 4.3Property-by-property transfer in L3 phonology
    • 4.4Restructuring a contrastive hierarchy
  • 5.Principles of restructuring
    • 5.1Acquiring a new highly-ranked feature
    • 5.2Back to Mandarin
    • 5.3Individual variation
  • 6.Learning features
  • 7.Conclusion
  • Notes
  • References
Published online: 4 July 2023
DOI logo
https://doi.org/10.1075/lab.22051.arc
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