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 to successfully learn the categories in the input. When competition was added to the MOG architecture, this class of models successfully accounted for developmental enhancement and loss of sensitivity to phonetic contrasts. Moreover, the MOG with competition model was used to explore a potentially important distributional property of early speech categories --sparseness --in which portions of the space between phonetic categories is unmapped. Sparseness was found in all successful models and quickly emerged during development even when the initial parameters favored continuous representations with no gaps. The implications of these models for phonetic category learning in infants are discussed. Infants face a difficult problem in acquiring their native language because the acoustic/ phonetic variability in the input far exceeds the limited number of distinctive differences that define language-specific phonemes. How do infants attend to the relevant information that distinguishes words? Recent evidence suggests that phonemic categories may be induced, in whole or in part, by a rapid statistical learning mechanism that is sensitive to the distributional properties of phonetic input (Maye, Werker & Gerken, 2002; Maye, Weiss & Aslin, 2008). This evidence suggests that the detailed frequency-of-occurrence of tokens along continuous speech dimensions plays a crucial role in the formation and modification of phonemic categories.
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