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Chapter 2. Dynamic systems methods in the study of language acquisition

Modeling and the search for trends, transitions and fluctuations
  • Paul van Geert
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Abstract

Language acquisition is viewed as an example of a dynamic system. It consists of many components that interact with each other. The components show trajectories over time, the properties of which result from the dynamics of the interaction. A large variety of components can be taken as potential indicators of underlying mechanisms of change and acquisition. Examples of such indicators are the number of one-, two-, three- and more word sentences, the number of spatial prepositions, and many others. These and other observable aspects may be used as stochastic indicators of underlying processes such as transitions between qualitatively distinct generative mechanisms, discontinuities, and so forth. Insights into the dynamics of language acquisition may be obtained, first, by modeling the dynamic interactions between the components at issue and by comparing qualitative properties of data simulated by those models with properties of empirical data. A second approach to obtaining more insight into the dynamics of language acquisition is by applying flexible smoothing techniques to time-serial language data and to determine the eventual changes in the amount of fluctuation in the data. Both the smoothed curves and fluctuation data can provide indirect evidence of underlying processes, such as continuities or discontinuities and regressions. The modeling, smoothing, and fluctuation techniques are primarily quantitative and should be seen as an addition to qualitative analyses.

Abstract

Language acquisition is viewed as an example of a dynamic system. It consists of many components that interact with each other. The components show trajectories over time, the properties of which result from the dynamics of the interaction. A large variety of components can be taken as potential indicators of underlying mechanisms of change and acquisition. Examples of such indicators are the number of one-, two-, three- and more word sentences, the number of spatial prepositions, and many others. These and other observable aspects may be used as stochastic indicators of underlying processes such as transitions between qualitatively distinct generative mechanisms, discontinuities, and so forth. Insights into the dynamics of language acquisition may be obtained, first, by modeling the dynamic interactions between the components at issue and by comparing qualitative properties of data simulated by those models with properties of empirical data. A second approach to obtaining more insight into the dynamics of language acquisition is by applying flexible smoothing techniques to time-serial language data and to determine the eventual changes in the amount of fluctuation in the data. Both the smoothed curves and fluctuation data can provide indirect evidence of underlying processes, such as continuities or discontinuities and regressions. The modeling, smoothing, and fluctuation techniques are primarily quantitative and should be seen as an addition to qualitative analyses.

Chapters in this book

  1. Prelim pages i
  2. Table of contents v
  3. Introduction. New perspectives in the study of first and second language acquisition 1
  4. Part I. Emergence and dynamics of language acquisition and disorders
  5. Chapter 1. A tale of two paradigms 17
  6. Chapter 2. Dynamic systems methods in the study of language acquisition 33
  7. Chapter 3. Early bootstrapping of syntactic acquisition 53
  8. Chapter 4. Language acquisition in developmental disorders 67
  9. Part II. First language acquisition
  10. Chapter 5. Language development in a cross-linguistic context 91
  11. Chapter 6. A typological approach to first language acquisition 109
  12. Chapter 7. Linguistic relativity in first language acquisition 125
  13. Chapter 8. On the importance of goals in child language 147
  14. Chapter 9. Promoting patients in narrative discourse 161
  15. Chapter 10. On-line grammaticality judgments 179
  16. Chapter 11. The expression of finiteness by L1 and L2 learners of Dutch, French, and German 205
  17. Part III. Bilingualism and second language acquisition
  18. Chapter 12. Age of onset in successive acquisition of bilingualism 225
  19. Chapter 13. The development of person-number verbal morphology in different types of learners 249
  20. Chapter 14. Re-thinking the bilingual interactive-activation model from a developmental perspective (BIA-d) 267
  21. Chapter 15. Foreign language vocabulary learning 285
  22. Chapter 16. Cerebral imaging and individual differences in language learning 299
  23. Chapter 17. The cognitive neuroscience of second language acquisition and bilingualism 307
  24. Index of languages 323
  25. Index of subjects 325
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