Connectionist Modeling of Experience-based Effects in Sentence ...

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3.3 A Model of RC Processing to human learning. However, Christiansen and Chater (1999) showed that an SRN is an outstanding predictor, especially for complex embedding issues. Chapter 4 will now show useful SRN predictions on two further topics. 61
Chapter 4 Two SRN Prediction Studies This chapter is concerned with the connectionist simulation of the subject/object difference in Mandarin and the forgetting effect in English and German. As has been found in the previous chapters, detailed predictions of an experience-based account are important both the subject/object difference in Chinese and the language-specific forgetting effect. For the forgetting effect a structural experience account seems to be a promising predictor capturing the divergence of the effects in different languages. Similarly, in the Mandarin extraction preference question structural regularities are considered as an important explanatory factor. As discussed in 1.3, the problem of theories involving structural experience or canonicity is the justification of certain granularity commitments. Chapter 3 emphasized that an implementation of the experience account in a connectionist network model deals with the granularity problem in a natural way, leaving it to the learning process to extract structural information on the granularity level that best serves the solution of the task. The network model by MacDonald and Christiansen (2002) (MC02) discussed in chapter 3 as being a prototypical structural experience implementation has proven to make empirically consistent predictions regarding the subject/object difference and individual differences. On that basis I rebuilt the model and used it to address the above mentioned issues that were waiting for a connectionist answer. In order to verify my implementation of the model, I present replications of the English and German simulations before reporting the new simulations. But first I will briefly introduce the structure of the network. 4.1 The Model 4.1.1 Network Architecture As in MC02 the connectionist architecture used was a simple recurrent network (SRN Elman, 1990) as described in chapter 3. All networks were built, trained, and tested in the Tlearn simulator (Elman, 1992) on a Windows platform. The SRN consisted of four layers. 62
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Connectionist Modeling of Experienc
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Acknowledgments I am grateful to Be
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Contents 3.3.4 Summary . . . . . .
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List of Tables 2.1 Languages with s
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Chapter 1 Preliminaries and the ACT
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Chapter 1 Preliminaries (1) a. The
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1.3 Psycholinguistic Aspects Chapte
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Chapter 1 Preliminaries referrentia
Page 17 and 18: Chapter 1 Preliminaries empirically
Page 19 and 20: Chapter 1 Preliminaries rial, in Le
Page 21 and 22: Chapter 1 Preliminaries are limited
Page 23 and 24: Chapter 1 Preliminaries predictor.
Page 25 and 26: Chapter 2 Issues in Relative Clause
Page 45 and 46: clear predictions for head-final RC
Page 51 and 52: Reading time [ms] 400 600 800 1000
Page 55 and 56: OUTPUT PUT PLAN Chapter 3 Connectio
Page 57 and 58: Chapter 3 Connectionist Modelling o
Page 63 and 64: German Word Order Chapter 3 Connect
Page 65 and 66: Length-Adjusted Reading Time (ms) C
Page 67: Chapter 3 Connectionist Modelling o
Page 71 and 72: 4.1.3 Training and Testing Chapter
Page 73 and 74: GPE 0.0 0.2 0.4 0.6 0.8 1.0 English
Page 75 and 76: (23) [V1 [N1 V2 de ORC ] N2 de SRC
Page 77 and 78: Chapter 4 Two SRN Prediction Studie
Page 85 and 86: (27) German with commas: a. SRC: S1
Page 87 and 88: 4.4.4 Discussion Chapter 4 Two SRN
Page 91 and 92: Bibliography M. H. Christiansen. Th
Page 93 and 94: Bibliography E. Gibson and J. Thoma
Page 95 and 96: Bibliography J. W. King and M. Kuta
Page 97 and 98: Bibliography F. Reali and M. H. Chr
Page 99 and 100: Appendix A Statistics SRC ORC regio
Page 101 and 102: Appendix B Grammars B.1 English (wr
Page 103 and 104: B.2 German {numREL, Nnom, RC RCpure
Page 105 and 106: B.3 Mandarin Rel : SRC (0.85) | ORC
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