This collection of articles and associated discussion papers focuses on a problem that has attracted increasing attention from linguists and psychologists throughout the world during the past several years. Reduced to essentials, the problem is that of discovering the character of the mental capacities that make it possible for human beings to attain knowledge of their language on the basis of fragmentary and haphazard early linguistic experience. A fundamental assumption running through all of these contributions is that people possess strong innate predispositions that are critical for success in this task.
This volume explores how a second language is acquired and what learners must do in order to achieve proficiency. The hardback edition is a collection of original essays that approaches second language acquisition from a linguistic rather than a sociological, psychological, or purely pedagogical perspective. A wide range of viewpoints and approaches is represented. However, all authors agree on the fundamental importance of linguistic theory in the study of second language acquisition. Few works have explored in depth how a second language is acquired and what the second language learner must do mentally to achieve proficiency in another language. The essays in this book provide an incisive analysis of these questions. For greater accessibility, the chapters are arranged topically from those covering the broad area of theories of acquisition to those focusing specifically on syntax, semantics, pragmatics, lexicon, and phonology in another language.
Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky. These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two areas almost never overlap. The book's objective is to bridge this gap. It uses the formal techniques developed in statistical learning theory and theoretical computer science over the last decade to analyze both kinds of learning problems. By asking the same question - how much information does it take to learn? - of both problems, it highlights their similarities and differences. Specific results include model selection in neural networks, active learning, language learning and evolutionary models of language change. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the interaction of computer science and cognitive science should enjoy the book. Researchers in artificial intelligence, neural networks, linguistics, theoretical computer science, and statistics will find it particularly relevant.
The main focus of generative language development research in recent decades has been the logical problem of language acquisition - how learners go beyond the input to acquire complex linguistic knowledge. This collection deals with the complementary issue of the developmental problem of language acquisition: How do learners move from one developmental stage to another and how and why do grammars develop in a certain fashion? Building on considerable previous research, the authors address both general and specific issues related to paths of development. These issues are tackled through considering studies of L1 and L2 children and L2 adults learning a range of languages including Dutch, English, French, German, Greek and Japanese.
