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Developmental Science 12:3 (2009), pp 369378 DOI: 10.1111/j.1467-7687.2009.00822.x 2009 The Authors. Journal compilation 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and
 

Summary: Developmental Science 12:3 (2009), pp 369­378 DOI: 10.1111/j.1467-7687.2009.00822.x
İ 2009 The Authors. Journal compilation İ 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and
350 Main Street, Malden, MA 02148, USA.
Blackwell Publishing Ltd
SPECIAL SECTION: COMPUTATIONAL PRINCIPLES
OF LANGUAGE ACQUISITION
Statistical learning of phonetic categories: insights from
a computational approach
Bob McMurray,1
Richard N. Aslin2
and Joseph C. Toscano1
1. Department of Psychology and the Delta Center, University of Iowa, Iowa City, USA
2. Department of Brain and Cognitive Sciences, University of Rochester, Rochester, USA
Abstract
Recent evidence (Maye, Werker & Gerken, 2002) suggests that statistical learning may be an important mechanism for the
acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its
implications for development by implementing a statistical learning mechanism in a computational model based on a mixture
of Gaussians (MOG) architecture. Statistical learning alone was found to be insufficient for phonetic category learning ­ an
additional competition mechanism was required in order for the categories in the input to be successfully learnt. When competition
was added to the MOG architecture, this class of models successfully accounted for developmental enhancement and loss of sensitivity

  

Source: Aslin, Richard N. - Department of Brain and Cognitive Sciences, University of Rochester

 

Collections: Biology and Medicine