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Inductive inference in non-native speech processing and learning


Despite extensive research on language acquisition, our understanding of how people learn abstract linguistic structures remains limited. In the phonological domain, we know that perceptual reorganization in infancy results in attuning to native language (L1) phonetic categories and, consequently, in difficulty discriminating and learning non-native categories. This difficulty has been proposed to originate from novel sounds being perceptually mapped onto L1 phonetic categories, leading to massive L1 interference. However, ample evidence that the adult speech processing system preserves a considerable degree of plasticity suggests that more complex learning mechanisms might be in place. In this dissertation I propose an alternative theory in which non-native speech processing is guided by principles of hierarchical inductive inference regarding how likely a given phonetic dimension is to be phonologically informative in any novel language. This theory differs crucially from mapping theories in predicting that when a phonetic dimension is informative (e.g., phonologically contrastive) in one's native language, discriminations involving that dimension should be enhanced even among classes of sounds for which the dimension is not informative in the native language. I provide experimental evidence supporting the inductive theory, demonstrating that language learning goes beyond the acquisition of specific phonetic categories, and includes higher-order generalizations regarding the relative importance of phonetic dimensions in the language as a whole. I argue that this theory can be extended beyond phonetic category learning to other domains of language acquisition, and that it suggests that adults and infants recruit the same domain-general learning mechanisms when acquiring novel languages

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