Electrophysiological Evidence for Sentence Comprehension - Wings

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probable option. This explains why the parser takes the most preferable option as its choice for sentence interpretation in case of ambiguity. This also means that sometimes the less preferred options will be chosen instead. Serial and parallel processing models make different predictions about ambiguities. If the parser computes only one interpretation and if somehow takes the correct one, than there would be no increase of reaction time or reading time in an experimental situation. If the reaction time or reading time increased in the same situation, this meant that the parser computed more than one interpretation of the sentence. Significant differences in reaction times or reading times between sentences (3) and (4) indicates repair or reinterpretation processes (although ranking mechanisms in parallel processing models account for such differences, as well, see for example, Boland, 1997). Deep vs. shallow models. Processing models differ in the amount of syntactic information they claim is available for processing as the listener hears the sentence. Full sentence parsers provide a full syntactic tree as an output while shallow parsers provide only the major constituents of the sentence. For example given the sentence (6) The bank has powerful means and instruments for reaching its financial goals. a full sentence parser would give a complete tree-structure with all the dependencies on place. It would make clear whether the adjective powerful modifies the phrase means and instruments and not only means (i.e. whether the sentence structure includes [powerful [means and instruments]] or [[powerful means] and [instruments]]). Shallow parsers do 27
not provide such an output. In the sentence (6) it would just group [powerful means and instruments] together. In computational linguistics shallow parsers are viewed as an alternative to full-sentence parsing, an alternative with some practical advantages. Performing only partial syntactic analysis of the sentence can be useful in many large-scale language processing applications such as text summarization. When the concept of shallow parsing was conceived, learning methods were used to recognize patterns in sentences (Ramshaw & Marcus, 1995). The idea of pattern recognition is suitable for applications using neural networks and this makes it interesting for language comprehension modeling. Apart from applications in computational linguistics, psycholinguistic evidence for shallow parsing is available, as well (Sanford & Sturt, 2002). The evidence includes underspecifications (Reyle, 1993) as in the following three sentences: (7) Alice showed all her cameras to a technician. (8) The technician worked in a big store. (9) The technicians worked in a big store. Given the two possibilities, that there is only one technician and that there are more of them, there should be a difference in reading times between the pairs and showing preference for one possible interpretation. However, no such difference is observed (Sanford & Sturt, 2002: 383) suggesting that a phrase a technician is underspecified in (7). Other psycholinguistic evidence include lack of error detection (and providing wrong answers) in sentences such as 28
Page 1 and 2: University of Zagreb Interdisciplin
Page 3 and 4: TABLE OF CONTENT 1. Introduction 5
Page 5 and 6: Electrophysiological results 131 5.
Page 7 and 8: How people comprehend language is o
Page 9 and 10: 1.1. Sentence comprehension There i
Page 11 and 12: syntactic structure obtained by the
Page 13 and 14: this process many other factors pla
Page 15 and 16: exploring sentence comprehension re
Page 17 and 18: The major advantage of eye-tracking
Page 19 and 20: ERP waveform. Components are conven
Page 21 and 22: earlier syntactic component, early
Page 23 and 24: enzyme activity, receptors or trans
Page 25 and 26: the formulation of the models that
Page 27: and disposes the wrong one as soon
Page 31 and 32: itself) is called 'pragmatic normal
Page 33 and 34: grammatical knowledge, resembling p
Page 35 and 36: components. However, the models tha
Page 37 and 38: The ‘syntax-first’ model is obv
Page 39 and 40: in which any information is process
Page 41 and 42: into brain functions that are not n
Page 43 and 44: Figure 4. The layered structure of
Page 45 and 46: Activity verbs, in contrast, refer
Page 47 and 48: step in Levelt’s model perfectly
Page 49 and 50: language functioning and (2) scores
Page 51 and 52: are included, as well (e.g. the Bus
Page 53 and 54: Binding, to be more precise) to exp
Page 55 and 56: the changes in language related com
Page 57 and 58: esearch project, as well. The proje
Page 59 and 60: 2. AIMS AND PROBLEMS 58
Page 61 and 62: 2.2. Sentence Comprehension in Typi
Page 63 and 64: The hypotheses can be grouped accor
Page 65 and 66: 4. METHODS 64
Page 67 and 68: On the basis of the test results 6
Page 69 and 70: data collected within a group of ad
Page 71 and 72: Figure 10. The presentation of the
Page 73 and 74: The violation condition consists of
Page 75 and 76: In Croatian adjectives usually prec
Page 77 and 78: object). This constitutes the viola
Page 79 and 80:
‘tense’ experiments as well as
Page 81 and 82:
Phonological abilities were tested
Page 83 and 84:
. In addition, Homonyms, synonyms a
Page 85 and 86:
software was used. The caps employe
Page 87 and 88:
5.1. Adults 5.1.1. Behavioral resul
Page 89 and 90:
the processes underlying comprehens
Page 91 and 92:
Table 5. Statistical significance o
Page 93 and 94:
calculated by subtracting the non-c
Page 95 and 96:
assigned when the verb is presented
Page 97 and 98:
Figure 22. Average waveform for Pz
Page 99 and 100:
Table 8 gives the statistical signi
Page 101 and 102:
Figure 26. The LAN effect in the
Page 103 and 104:
Figure 28. P600 effect on the CPz e
Page 105 and 106:
Figure 30. The waveforms at the FCz
Page 107 and 108:
semantics distinction more relative
Page 109 and 110:
could, in fact, be the focus of the
Page 111 and 112:
Subject: Neuroscan EEG file: adults
Page 113 and 114:
Figure 38. The P600 in the ‘tense
Page 115 and 116:
Figure 41. Distribution maps in the
Page 117 and 118:
The results of the two experiments
Page 119 and 120:
Statistical analysis reveals P600 e
Page 121 and 122:
Subject: Neuroscan EEG file: chiten
Page 123 and 124:
Subject: EEG file: chitensechin2_1s
Page 125 and 126:
Subject: Neuroscan EEG file: casech
Page 127 and 128:
Figure 55. Difference in latency be
Page 129 and 130:
only relatively good instrument for
Page 131 and 132:
similar words or pseudo-words. Quic
Page 133 and 134:
Subject: Neuroscan EEG file: slicas
Page 135 and 136:
Subject: EEG file: slicasechi2_1s.a
Page 137 and 138:
Subject: Neuroscan EEG file: sli_te
Page 139 and 140:
Table 25. ANOVA results for the fro
Page 141 and 142:
Subject: Neuroscan EEG file: casech
Page 143 and 144:
Subject: Neuroscan EEG file: chiten
Page 145 and 146:
5.3. General Discussion 5.3.1. Sent
Page 147 and 148:
Croatian. In this respect ERP metho
Page 149 and 150:
Figure 72. Difference between the e
Page 151 and 152:
5.3.3.. The results in the light of
Page 153 and 154:
operator projection of the clause (
Page 155 and 156:
Leonard’s words, of ‘low phonet
Page 157 and 158:
the ‘case-chi’ experiment and t
Page 159 and 160:
Six ERP experiments were performed
Page 161 and 162:
corpus of data. Future work should
Page 163 and 164:
- Aaronson, D., Scarborough, H.S. (
Page 165 and 166:
- Bishop, D. V. M., Frazier-Norbury
Page 167 and 168:
- Conti-Ramsden, G., Jones, M. (199
Page 169 and 170:
- Frazier, L., & Rayner, K. (1982).
Page 171 and 172:
- Hagoort, P., Brown, C., Groothuse
Page 173 and 174:
- Jurafsky, D. (2002). Probabilisti
Page 175 and 176:
- Kovačević, M., Jelaska, Z., Kuv
Page 177 and 178:
- Molfese, D., Molfese, V, Key, A.
Page 179 and 180:
typically developing peers. Journal
Page 181 and 182:
- Rumelhart, D.E., McClelland, J.L.
Page 183 and 184:
- Tallal, P. (1976). Rapid auditory
Page 185 and 186:
- Weber, F. M., Friederici, A. D. (
Page 187 and 188:
LIST OF FIGURES Figure 1. The proce
Page 189 and 190:
Figure 54. Difference between adult
Page 191 and 192:
SUMMARY The method of event-related
Page 193 and 194:
EXTENDED SUMMARY IN CROATIAN SAŽET
Page 195 and 196:
teorija jezične obrade (razumijeva
Page 197 and 198:
(kad se događa radnja), taj se val
Page 199 and 200:
eferenci. Modularnost se, s druge s
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