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Learning the identity effect as an artificial language: bias and generalisation*

Published online by Cambridge University Press:  02 August 2013

Gillian Gallagher
Gillian Gallagher*
Affiliation:
New York University
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Abstract

The results of two artificial grammar experiments show that individuals learn a distinction between identical and non-identical consonant pairs better than an arbitrary distinction, and that they generalise the distinction to novel segmental pairs. These results have implications for inductive models of learning, because they necessitate an explicit representation of identity. While identity has previously been represented as root-node sharing in autosegmental representations (Goldsmith 1976, McCarthy 1986), or implicitly assumed to be a property that constraints can reference (MacEachern 1999, Coetzee & Pater 2008), the model of inductive learning proposed by Hayes & Wilson (2008) assumes strictly feature-based representations, and is unable to reference identity directly. This paper explores the predictions of the Hayes & Wilson model and compares it to a modification of the model where identity is represented (Colavin et al.2010). The results of both experiments support a model incorporating direct reference to identity.

Type
Articles
Information
Phonology , Volume 30 , Issue 2 , August 2013 , pp. 253 - 295
Copyright
Copyright © Cambridge University Press 2013 

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Footnotes

*

I am particularly grateful to Peter Graff and three anonymous reviewers for detailed comments on this work, and also to Tal Linzen for helpful discussion. I also thank participants in the Fall 2012 phonology seminar and the Phonetics and Experiment Phonology reading group at New York University.

References

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