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

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1.3 Psycholinguistic Aspects finally similarity-based interference is sometimes also said to affect processing between encoding and integration; this is called storage interference (Lewis et al., 2006). In contrast to locality effects in English opposite effects have been found in head-final languages. For example, Konieczny (2000), Vasishth and Lewis (2006b), and Gibson et al. (2005b) found so-called antilocality effects in German, Hindi, and Japanese, respectively. For example, Konieczny (2000) used German stimuli (4) that were comparable to those used by Grodner and Gibson (2005) in their English study. (4) a. Er hat den Abgeordneten begleitet, und . . . He has the delegate escorted, and . . . “He escorted the delegate, and . . . ” b. Er hat den Abgeordneten ans Rednerpult begleitet, und . . . He has the delegate to_the lectern escorted, and . . . “He escorted the delegate to the lectern, and . . . ” c. Er hat den Abgeordneten an das große Rednerpult begleitet, und . . . He has the delegate to the big lectern escorted, and . . . “He escorted the delegate to the large lectern, and . . . ” Reading times at the main verb begleitet showed fastest reading in (4c) and slowest in (4a). That is contradicting locality predictions. Since antilocality effects were first only discovered in head-final languages, it is commonly assumed that the language-specific word order regularities cause the divergent effects. Whereas theories based on integration cost (e.g. DLT: Gibson, 1998) cannot account for antilocality, expectation-based theories (Hale, 2001; Levy, 2008) predict antilocality effects to be caused by an increasing expectation of the verb to appear while more intervening material is read. Notably, expectation theories predict antilocality language-independently. Their predictions receive support by recent evidence (Jaeger, Fedorenko, Hofmeister, and Gibson, 2008) for the presence of antilocality even in non-head-final languages (for an overview of possible explanations concerning locality and antilocality see Vasishth, 2008). The Dependency Locality Theory Gibson (1998; 2000) formulated a theory of capacity and decay in working memory in a discrete symbolic fashion. The cost predictions were based on dependency-induced predictions of syntactic nodes and their distances, which is why the original theory was called the Syntactic Prediction Locality Theory (SPLT). The later revised version is called the Dependency Locality Theory, referred to as DLT. The DLT assigns a memory cost to each word in a sentence on the basis of two discrete functions: Integration Cost and Storage Cost. Integration Cost directly accounts for locality effects by a discrete distance measure. It predicts the amount of processing difficulty at the integration site and is defined by the number of discourse referents intervening between the dependent and its head. Valid discourse referents in Gibson’s sense are 7
Chapter 1 Preliminaries referrential constituents like nouns and main verbs as they refer to objects and events, respectively. Pronouns, however, do not induce memory cost because they are assumed to be immediately accessible. The assumption behind Integration Cost is that every stored item receives an activation which decays depending on the number of newly encoded discourse referents while it is maintained in memory. Integrating an element, i.e., relating it to its head, needs more processing effort when the element has less activation. Thus the integration cost is a function, monotonously increasing with the number of intervening discourse referents. The cost accounts only implicitly for decay over time since time is only represented discretely by successive discourse referents. The unit of Integration Cost is energy units (EUs). The memory capacity limit is accounted for by the second principle of DLT: Storage Cost. It rests on the assumption that the parser constantly predicts the most probable complete sentence structure given the previous material and keeps it in memory. Structural complexity is calculated by the number of syntactic heads contained. The more complex the predicted structure, the more syntactic heads it contains. Every predicted head uses up memory resources, so-called memory units (MUs). Memory load also affects processing, because storage and processing use the same resources (Just and Carpenter, 1992). Consequently, the more heads are predicted the higher the processing cost. The important difference between the two costs is the location of their effects. While Integration Cost accounts for processing differences only at the integration site, Storage Cost for a predicted structure affects processing of every following part of the sentence. Figure 1.1 shows the Integration Cost C(I) and the Storage Cost C(S) at each point in an English object relative clause. Seeing the sentence-initial determiner ORC The reporter whoi the senator attacked ei admitted the error C(I) 0 0 0 0 0 1+2 3 0 0+1 C(S) 2 1 3 4 3 1 1 1 0 Total 2 1 3 4 3 3 4 1 1 Figure 1.1: DLT cost metrics for an English ORC according to Gibson (1998). induces the prediction of a main clause. Hence predictions for an NP and a main verb have to be stored. Note that DLT considers the prediction of the main verb as cost-free, but in literature, it is mostly assigned a cost. For simplicity, in this work Storage Cost will be consistently assumed for the main verb. Having completed the NP only the verb is predicted. At the relative pronoun who a Storage Cost of 3 is assigned because an embedded SRC is predicted, containing two heads: the embedded verb and a subject gap. Seeing another determiner changes the prediction into an ORC, which contains one more head, namely the embedded subject. On senator only the embedded verb, the object gap, and the main verb stay predicted. On the embedded verb attacked then two integrations take place. The subject integration of attacked costs 1 EU because the 8
Page 1 and 2: Connectionist Modeling of Experienc
Page 3 and 4: Acknowledgments I am grateful to Be
Page 5 and 6: Contents 3.3.4 Summary . . . . . .
Page 7 and 8: List of Tables 2.1 Languages with s
Page 9 and 10: Chapter 1 Preliminaries and the ACT
Page 11 and 12: Chapter 1 Preliminaries (1) a. The
Page 13: 1.3 Psycholinguistic Aspects Chapte
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
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Length-Adjusted Reading Time (ms) C
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Chapter 3 Connectionist Modelling o
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Chapter 4 Two SRN Prediction Studie
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4.1.3 Training and Testing Chapter
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GPE 0.0 0.2 0.4 0.6 0.8 1.0 English
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(23) [V1 [N1 V2 de ORC ] N2 de SRC
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(27) German with commas: a. SRC: S1
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4.4.4 Discussion Chapter 4 Two SRN
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Bibliography M. H. Christiansen. Th
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Bibliography E. Gibson and J. Thoma
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Bibliography J. W. King and M. Kuta
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Bibliography F. Reali and M. H. Chr
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Appendix A Statistics SRC ORC regio
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Appendix B Grammars B.1 English (wr
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B.2 German {numREL, Nnom, RC RCpure
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B.3 Mandarin Rel : SRC (0.85) | ORC
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