ISBN: 9780262346313 | 392 pp. | July 2018

Changing Minds Changing Tools

From Learning Theory to Language Acquisition to Language Change
Overview

In this book, Vsevolod Kapatsinski argues that language acquisition—often approached as an isolated domain, subject to its own laws and mechanisms—is simply learning, subject to the same laws as learning in other domains and well described by associative models. Synthesizing research in domain-general learning theory as it relates to language acquisition, Kapatsinski argues that the way minds change as a result of experience can help explain how languages change over time and can predict the likely directions of language change—which in turn predicts what kinds of structures we find in the languages of the world. What we know about how we learn (the core question of learning theory) can help us understand why languages are the way they are (the core question of theoretical linguistics).

Taking a dynamic, usage-based perspective, Kapatsinski focuses on diachronic universals, recurrent pathways of language change, rather than synchronic universals, properties that all languages share. Topics include associative approaches to learning and the neural implementation of the proposed mechanisms; selective attention; units of language; a comparison of associative and Bayesian approaches to learning; representation in the mind of visual and auditory experience; the production of new words and new forms of words; and automatization of repeated action sequences. This approach brings us closer to understanding why languages are the way they are, Kapatsinski contends, than approaches premised on innate knowledge of language universals and the language acquisition device.

Table of Contents

  1. Acknowledgments
  2. Introduction
  3. 1. The Web in the Spider: Associative Learning Theory
  4. 2. From Associative Learning to Language Structure
  5. 3. What Are the Nodes? Unitization and Configural Learning vs. Selective Attention
  6. 4. Bayes, Rationality, and Rashionality
  7. 5. Continuous Dimensions and Distributional Learning
  8. 6. Schematic Structure, Hebbian Learning, and Semantic Change
  9. 7. Learning Paradigmatic Structure
  10. 8. The Interplay of Syntagmatic, Schematic, and Paradigmatic Structure
  11. 9. Automatization and Sound Change
  12. 10. Bringing It All Together
  13. Notes
  14. References
  15. Index