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

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3.3 A Model of RC Processing MC02’s SRN predictions. For that reason two participant groups were formed: an RC experience group and a control experience group, both matched on reading span. Both groups received reading training over 3-4 weeks. The RC experience group received training mainly on SRC and ORC structures whereas the control experience group was exposed to other structures. A pre- and a post-test carried out in the SPR paradigm assessed both groups RC reading performance. The results show an overall improvement of RC reading. Most importantly, however, the data revealed an interaction of session × experience group × clause type × region as can be seen in figure 3.4. There was a reliable difference between SRC and ORC reading times observed in both groups in the pre-test; in particular the ORC condition was read slower. The subject/object difference, however, decreased significantly between pre- and post-test for the RC experience group whereas in the control group it stayed the same. Wells et al. attribute the global reading improvement to increased familiarity with the SPR task. The observed pattern in the experience group is similar, first, to the span × clause type interaction in the study by King and Just (1991) reported in 2.1, and second, it resembles the frequency × regularity interaction of the connectionist model in MC02 (see figure 3.5). Statistically the SRNs’ mean GPE scores predicted the within-sentence and experience-based variance in the human data extraordinarily well. There was a total fit of GPE and reading times of R 2 = .75. In a hierarchical regression Wells and colleagues also predicted the SRN simulation results using the human reading times. The RC experience group data accounted for 75% of overall variance in the SRNs’ GPE scores. Using the control group data as a predictor, which did not involve an experience factor but merely within-sentence variance, the regression accounted only for about 65% of the GPE variance. The impressive result of Wells et al.’s study delivers empirical support for the implications drawn by MC02 from the SRN simulations, namely that experience can account for individual differences in reading skill. Notably, the Wells et al. study proved a significant experience effect after minimal exposure amount (the training sets contained only 160 sentences in total). Furthermore, lexical and structural short term priming effects were excluded by, first, a four weeks test distance, second, no lexical overlap between the tests and, third, the usage of RC constructions during training that were structurally different from testing items. Also task-related adaptation can be excluded since during the training phase a full-sentence display was used instead of word-by-word reading. Now, given the evidence that reading skill on certain sentence structures is affected by previous experience with these structures it remains the question of what has been learned; a question that evolves from the granularity problem of exposure-based theories described in section 1.3.4. Wells and colleagues identify verb transitivity as a crucial factor driving the learning process of the SRN. In ORCs on the one hand, embedded verbs are necessarily transitive because the head noun has to fill an object role. On the other hand, simple sentences and SRCs occur with transitive and intransitive verbs. That has the consequence that only for continuation predictions of ORCs the network has to learn to differentiate verb transitivity in the predictions whereas for the continuation 57
Length-Adjusted Reading Time (ms) Chapter 3 Connectionist Modelling of Language Comprehension Figure 1: Self-Paced Reading Patterns at Pre- and Posttest 175 150 125 100 75 50 25 0 -25 -50 -75 1 2 3 4 OR: (The) clerk that the typist trained told the truth SR: (The) clerk that trained the typist told the truth 36 Relative Clause Experience RC Experience Group (n=32) Control Experience Group (n=32) Pretest, Object Relatives Pretest, Subject Relatives Posttest, Object Relatives Posttest, Subject Relatives 175 150 125 100 75 50 25 0 -25 -50 -75 1 2 3 4 (The) clerk that the typist trained told the truth (The) clerk that trained the typist told the truth Figure 3.4: Wells et al. (2009) reading times for pre- and post-test by group and RC type. of other structures transitive and intransitive verbs are of equal probability. Example (20) shows structural prefixes in main clauses (20a), SRCs (20b), and ORCs (20c) with possible transitivity properties of predicted verbs. (20) a. Simple: EOS the N . . . {trans/intrans} b. SRC: the N that . . . {trans/intrans} c. ORC: (the N) that the N . . . {trans} The experience effect of human readers, however, can be affected by a lot more structural cues in the input. A crucial factor also mentioned for Chinese RCs in section 2.4 is animacy. In English like in Mandarin ORCs mostly contain inanimate head nouns whereas in SRCs head nouns are commonly animate. Since Wells et al. only used animate head nouns for both SRCs and ORCs the participants might have learned to handle the non-canonical animate-headed ORCs. Race and MacDonald (2003), Reali and Christiansen (2007a), and Reali and Christiansen (2007b) identify further probabilistic constraints that correlate with SRC/ORC corpus distributions. For example pronominal ORCs mostly contain personal pronouns whereas impersonal pronouns occur more frequently in SRCs. Furthermore there are differences in the NP-type of the embedded subjects that separate ORCs from other structures that exhibit an ‘NP that NPSubj VP’ sequence. The usage of a pronoun in the NPSubj position is highly correlated with ORCs. The Wells et al. study only used common nouns and all RCs were headed by the impersonal pronoun that, which both are potential properties subject to probabilistic learning since they are deviations from natural frequency patterns. 58
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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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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: German Word Order Chapter 3 Connect
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 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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