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

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4.5 Conclusion of the VP-predicting NP in the representational cycle of the SRN decays faster. This in turn is compensated by increased training on center-embedding compared to the English simulation, resulting in comparable error values when trained without commas. This is, of course, an ad-hoc hypothesis and needs further investigation, which is beyond the scope of this thesis. 4.5 Conclusion This thesis investigated the explanatory power of a certain implementation of the experience account. The well approved SRN modeling approach of MacDonald and Christiansen (2002) was adopted to test its predictions on two phenomena currently discussed in literature. The RC extraction type preference in Mandarin and the forgetting effect in complex center-embedding was discussed and then modeled. At first, the two problems were approached theoretically, reviewing results of empirical studies and discussing potential predictions of available theories. Concerning the Mandarin relative clauses, the studies showed exceptionally mixed results. However, an observed object advantage appeared always on the RC region, whereas a subject advantage was found only on the relativizer/head noun region. That fact and the experiment by Qiao and Forster (2008) are suggesting that Chinese Mandarin might have to be counted as an exception to a universal subject preference. On the other hand, the results for the forgetting effect were very clear and best explained by language-specific experience. In chapter 3 the simple recurrent network was introduced and its properties were discussed. An SRN is a very simple and domain-unspecific model, but it accounts for the three necessities introduced in the beginning of this thesis: a) biological factors (architectural limits), b) continuous gradedness in performance, and c) experience. In chapter 4 the experience theory predictions regarding the two sample problems were practically assessed. Just like in the discussion and critique of MC02, the simulation results presented here looked promising on first sight; but sub-experiments and detailed data analysis revealed considerable inconsistencies with respect to human data. The Mandarin RC simulation predicted an object preference, but the location of the effect was not consistent with human data. In addition, the second simulation demonstrated that the regularity effect was very weak. It becomes clear that the training material used must be carefully chosen in order to guarantee comparability with other simulations and empirical studies. The forgetting effect was predicted to be present in English but not in German, consistent with human data. However, further simulations revealed the comma insertion as the most important factor. Of course, it has to be clear that the simple network trained on a simple grammar would not learn the same constraints as humans do. These simulations are rather approximations pointing to a certain direction. A noticeable problem of the SRN predictions is their dependency on local coherence, which can also be described as a low memory span. This is, however, mainly dependent upon the specific properties of the learning 81
Chapter 4 Two SRN Prediction Studies mechanism and the context loop. As mentioned in the previous chapter, there are other learning mechanisms that can increase the span; although they might be cognitively very unmotivated. Interestingly, however, there is evidence that even human readers rely on local coherence in certain structures (Tabor et al., 2004). Another finding is that the simulations reported in Christiansen and Chater (1999) and also the comma issue in simulations 3 and 4 presented here showed that the SRN handles counting-recursion better than other types. That may be the reason for the strong facilitating effect of comma insertion compared to head-finality. Addressing this, it shall be noted that Rodriguez (2001) claims that SRNs can in fact carry out explicit symbolic counting procedures. This work argued for a uniform account to individual and language-specific differences as well as language-independent processing skill. All three can in considerable parts be attributed to experience with the individual linguistic environment in interaction with architectural preconditions. It can be concluded that a lot of work is necessary before fine-grained experience-based predictions can be gained for the highly complex task of sentence comprehension. By all means, literature shows a promising trend towards PDP models of language comprehension, accompanied by the integration of corpus analyses and acquisition data. 82
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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
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Chapter 1 Preliminaries empirically
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Chapter 1 Preliminaries rial, in Le
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Chapter 1 Preliminaries are limited
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Chapter 1 Preliminaries predictor.
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Chapter 2 Issues in Relative Clause
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Page 37 and 38: 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 69 and 70: Chapter 4 Two SRN Prediction Studie
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 85 and 86: (27) German with commas: a. SRC: S1
Page 87: 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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