Yu-Leng Lin Lin Substantive Bias and Natural Classes

Substantive Bias and Natural Classes

von Yu-Leng Lin

An Empirical Approach

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Beschreibung

This book offers a laboratory phonological analysis of the sonority hierarchy and natural classes in nasal harmony using an artificial grammar-learning paradigm. It is aimed at postgraduate students and linguists in general whose research interests lie in phonology, phonetics, and/or  psycholinguistics. It is useful for linguists who are struggling to figure out how to effectively design an artificial phonological grammar and those who have not designed experiments on their own but would like to do so as an additional means to testing linguistic theories. This book is also a valuable resource for anyone building crosslinguistic artificial grammar paradigm resources.
This book offers a laboratory phonological analysis of the sonority hierarchy and natural classes in nasal harmony using an artificial grammar-learning paradigm. It is aimed at postgraduate students and linguists in general whose research interests lie in phonology, phonetics, and/or  psycholinguistics. It is useful for linguists who are struggling to figure out how to effectively design an artificial phonological grammar and those who have not designed experiments on their own but would like to do so as an additional means to testing linguistic theories. This book is also a valuable resource for anyone building crosslinguistic artificial grammar paradigm resources.
Provides linguists with guidelines on how to improve their design of artificial grammar learning Contributes to a better understanding of phonological patterning in Chinese linguistics Takes into account individual learning strategies in addition to pooled data

Autor*in

Yu-Leng Lin

Themen in »Substantive Bias and Natural Classes«

Nasal Harmony Substantive Bias Artificial Grammar Learning Sonority Hierarchy Natural Classes

Stimmen zu »Substantive Bias and Natural Classes«

Details

ISBN: 9789811335341
Verlag: Springer Singapore
Erscheinung: 10.01.2019

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