臺灣博碩士論文加值系統

音形對應一致性效果穩定的出現在口語辨識作業中，此結果支持了閱讀的經驗會影響到語音處理。然而，此效果是導因於字形表徵於顳枕視覺皮質區的共同激發或是文字學習在聽覺神經網路區對既有語音表徵的重新建構，至今仍未有定論。此外，目前也不清楚形音一致性效果是否也存在於口語辨識中。超過80%中文語音可以對應到多個字形，因此，相較於拼音文字， 同音字眾多的特性讓音形對應一致性在中文口語辨識中或許更能彰顯其影響。過去事件相關電位的研究已指出有兩種音形對應一致性效應會影響中文口語辨識:音形一致性(同音字是否有相同音旁)和同音字密度(字音可以對應到字的多寡)。實驗一和實驗二旨在運用功能性磁振造影，探討中文音形一致性和同音字密度效果在語意和押韻判斷作業下影響口語辨識的神經機制。結果顯示音形一致性效果在兩個作業下皆出現在聽覺神經網路區，包含額下迴、顳上迴及腦島，表示次字彙(sublexical)層次的字形對既有語音表徵產生重新架構的作用。然而，同音字密度效果只在語意判斷作業下出現在顳枕視覺皮質區，表示整字層次的字形會在口語辨識中同時激發。實驗三和四利用事件相關電位和腦磁波探討中文形音一致性(相同音旁的字是否有相同發音)在口語辨識中所扮演的角色，和所涉及的腦區活動隨時間變化的情況，以及如何受到同音字密度的影響。結果發現形音一致性效果發生在N400以及左側額下、顳頂和枕顳區域，並同時受到同音字密度的影響。另外，700毫秒以後，形音一致性效果也發生在涉及形音對應處理的腦區。同音字密度效果則發生在LPC和枕顳交界處。聽覺作業下的形音一致性效應支持了被激發字形本身與字音對應一致性也會影響到口語辨識。總結來說，本篇論文提供了趨於一致的證據支持了中文口語辨識會受到字形與字音雙向的對應一致性的影響。

The phonology-to-orthography (P-to-O) mapping consistency effect on spoken word recognition is a well-established phenomenon to support that reading experience shapes the speech processing. However, the issue under debate is whether the P-to-O consistency effect was resulted from the orthographic co-activation located in temporo-occipital visual cortex or the phonological restructuring located within the speech network. Meanwhile, it is also unclear whether the second-ordered effect, the orthography-to-phonology (O-to-P) mapping consistency, could be evident in spoken word recognition. In Chinese, over 80% of Mandarin syllables can be mapped onto multiple characters. Given the pervasive homophony of Chinese, one might expect a greater impact from orthography during speech in Chinese than that in alphabetic writing system. Two types of orthographic effect have been demonstrated in Chinese spoken word recognition with ERP (Chen et al., 2016). One is the P-to-O consistency (POC, whether a set of homophones can be divided into subgroups based on their phonetic radicals) and the other one is the homophone density for the mapping between phonology and orthography (HD, the number of characters sharing exactly the same pronunciation). Experiments 1 and 2 aimed to examine the neural mechanism underlying the two types of orthographic effects on Chinese spoken language under the semantic category and the rhyming judgment tasks with the event-related fMRI. The POC effects were observed within the perisylvian speech network, including inferior frontal gyrus (IFG), superior temporal gyrus (STG), and insula, in both tasks. The finding suggests that the sublexical orthographic information could restructure the preexisting phonological representations. In contrast, the HD effects in the left fusiform and lingual gyrus were only evident in the semantic judgment task, suggesting that the lexical orthographic knowledge may be activated on-line during spoken word recognition. Experiment 3 and 4 applied the ERP and magnetoencephalography (MEG) to identify the temporal and spatial loci of the second-order O-to-P consistency (OPC, the reliability of a phonetic radical in providing the whole character’s phonological clue) on Chinese spoken word recognition and to explore when and how this effect interacts with the first-order P-to-O mapping (i.e., HD). The OPC effect was modulated by HD during the N400 time window in the left inferior frontal, temporoparietal, and occipitotemporal regions. Moreover, the MEG data additionally revealed a later OPC effect in the activity of a set of brain regions that involved in statistical mapping between orthography and phonology after ~700 ms. The HD effects were found in the LPC time window (700-900 ms) in the left lingual gyrus. The second-order O-to-P consistency effect in the auditory modality supports the reverberation between orthography and phonology in Chinese spoken word recognition. Taken together, the dissertation provides convergent evidence to support that Chinese spoken word recognition can be influenced by the bidirectional mapping consistency between orthography and phonology.

中文摘要 i
Abstract ii
Table of Content iv
List of Tables vii
List of Figures ix
1 Introduction 1
1.1 The bimodal interactive activation model (BIAM) for word recognition 2
1.2 The temporal locus of the P-to-O consistency effect 5
1.3 Phonological restructuring versus orthographic co-activation 9
1.4 The neural correlates of the P-to-O consistency effect 10
1.5 The second-order O-to-P consistency effect in the spoken word recognition 12
1.6 The implement of the cross-code consistency framework in Chinese 14
1.7 Aims of the dissertation 17
2 Experiment 1: Neuronal correlates of P-to-O consistency effects on Chinese spoken word recognition 19
2.1 Introduction 19
2.2 Methods 20
2.2.1 Participants 20
2.2.2 Experimental Design 21
2.2.3 Procedures 22
2.2.4 MRI data acquisition 22
2.2.5 FMRI data preprocessing and statistical analysis 23
2.2.6 Functional connectivity analysis: gPPI 24
2.3 Results 25
2.3.1 FMRI data 25
2.3.2 Functional connectivity data 27
2.4 Discussion 31
2.4.1 Chinese orthographic effects on brain function 31
2.4.2 Chinese orthographic effects on functional connectivity 34
2.5 Conclusion 35
3 Experiment 2: Orthographic influences on Chinese spoken language in the brain: task-specific consistency effects as revealed by event-related fMRI 36
3.1 Introduction 36
3.2 Methods 37
3.2.1 Participants 37
3.2.2 Experimental Design 37
3.2.3 Procedures 39
3.2.4 MRI data acquisition and analysis 39
3.2.5 FMRI data preprocessing and statistical analysis 40
3.2.6 Functional connectivity analysis: gPPI 41
3.3 Results 41
3.3.1 Behavioral data 41
3.3.2 FMRI data 42
3.3.3 Functional connectivity data 44
3.4 Discussion 47
3.5 Conclusion 49
4 Experiment 3: The second-order effect of orthography-to-phonology mapping consistency on Chinese spoken word recognition 51
4.1 Introduction 51
4.2 Experiment 3-1: norming study 53
4.2.1 Methods 53
4.2.2 Results 58
4.2.3 Discussion 63
4.3 Experiment 3-2: ERP study 66
4.3.1 Methods 67
4.3.2 Results 72
4.3.3 Discussion 77
4.4 General Discussion 79
5 Experiment 4: Spatial and temporal dynamics of homophone density and orthography-to-phonology mapping consistency effects in writing Chinese characters: an MEG study 81
5.1 Introduction 81
5.2 Methods 82
5.2.1 Participants 82
5.2.2 Experimental Design 82
5.2.3 Procedures 83
5.2.4 Data acquisition 84
5.2.5 MEG data preprocessing and EEMD decomposition 84
5.2.6 Sensor-Level Statistics 85
5.2.7 Source analysis 87
5.2.8 Regions of interests (ROIs) and statistical analysis 87
5.3 Results 89
5.3.1 Behavioral results 89
5.3.2 MEG sensor data 90
5.3.3 The dynamic statistical parametric maps data 90
5.3.4 Results of ROI analyses 92
5.4 Discussion 94
5.5 Conclusion 98
6 General discussion 99
6.1 Summary of overall results 99
6.2 The implications of the bimodal interactive activation model (BIAM) for Chinese spoken word recognition 100
6.3 The role of ventral occipitotemporal cortex in Chinese spoken word recognition 105
6.4 Functional interactions of the left inferior frontal cortex during Chinese spoken word recognition 107
6.5 Limitations and future direction 110
References 112
Table 2.1 Means and standard deviations of parameters for stimuli. 22
Table 2.2 Peak activation of the contrasts for HD and POC effects 26
Table 2.3 Brain regions showing stronger connectivity with the left pars opercularis (pOper) seed for the HD and POC effects. 28
Table 2.4 Brain regions showing stronger connectivity with the left pars triangularis (pTri) seed for the HD and POC effects. 29
Table 2.5 Brain regions showing stronger connectivity with the left pars orbitalis (pOrb) seed for the HD and POC effects. 30
Table 3.1 Means and standard deviations of parameters for stimuli. 38
Table 3.2 Means and standard deviations of parameters for paired characters 38
Table 3.3 Means and standard deviations of the reaction times (ms) and accuracy (%) for each condition. 42
Table 3.4 Linear mixed model (LMM) estimates of the fixed effects for (A) reaction times and (B) accuracy. 42
Table 3.5 Peak activation of contrasts for the POC effect 43
Table 3.6 Brain regions showing stronger connectivity with the left pOper seed for the HD and POC effects 45
Table 3.7 Brain regions showing stronger connectivity with the left pTri seed for the HD and POC effects. 46
Table 3.8 Brain regions showing stronger connectivity with the left pOrb seed for the HD and POC effects. 47
Table 4.1 Normative data of the norm for generation probability (%). 56
Table 4.2 Means, standard deviations, and correlation coefficients of the predictors for generation probability and reaction time. 59
Table 4.3 Linear mixed model estimates of the fixed effects for (A) generation probability and (B) reaction time. 62
Table 4.4 Means and standard deviations of the accuracy (%) of written responses in each condition. 72
Table 4.5 Linear mixed model estimates of the fixed effects for (A) accuracy and (B) generation probability in the ERP study. 73
Table 4.6 Linear mixed model estimates of fixed effects for the average amplitudes in P200 (245–295 ms), N400 (450–650 ms) and the LPC (700–900 ms). 74

Table 5.1 Means and standard deviations of the accuracy (%) of written responses in each condition. 89
Table 5.2 Linear mixed model estimates (LMM) of the fixed effects for (A) accuracy and (B) generation probability in the MEG study. 89
Table 5.3 LMM estimates of fixed effects for significant effects of the interaction between HD and O-to-P consistency (OPC) in (a) 450 ms - 650 ms and (b) 700 ms - 900 ms and a significant effect of HD in (b) 700 ms - 900 ms time windows 90
Table 5.4 Summary of ROI analysis 92
Table 6.1 Temporal and Spatial results of the sublexical bidirectional mappings between phonology and orthography. 102
Table 6.2 Temporal and Spatial results of the lexical mappings between phonology and orthography. 104
Figure 1.1 The architecture of the bimodal interactive activation model (BIAM) for word recognition and the schema for bi-directional activation between orthography and phonology. 2
Figure 1.2 Examples for O-to-P consistency in English. 3
Figure 1.3 Examples for P-to-O consistency in English. 4
Figure 1.4 Examples for the bidirectional mapping consistency between orthography and phonology in English and Chinese (C. Y. Lee et al., 2015). 16
Figure 2.1 Activation maps for (A) three phonology-to-orthography (P-to-O) mapping conditions (P < 0.05, FWE-corrected) and the contrasts for (B) HD effects (P < 0.001, uncorrected) and (C) POC effects (P < 0.005, uncorrected) 25
Figure 2.2 Results of the gPPI analysis across the three conditions for the seed regions including the left pars opercularis (pOper), pars triangularis (pTri) and pars orbitalis (pOrb) of inferior frontal gyrus (IFG) (P < 0.05, FWE-corrected). 28
Figure 3.1 Activation maps for (A) three P-to-O mapping conditions (P < 0.05, FWE-corrected) and the contrasts for (B) POC effects (P < 0.001, uncorrected). 43
Figure 3.2 Results of the gPPI analysis across three treatments for the seed regions of the left pars opercularis (pOper), pars triangularis (pTri), pars orbitalis (pOrb) of the IFG (P < 0.05, FWE-corrected). 44
Figure 4.1 ERMs for high O-to-P consistency characters (high OP, solid lines) and low O-to-P consistency characters (low OP, dashed lines) at the represented electrodes under (a) low homophone density (low HD) and (b) high homophone density (high HD) conditions. 75
Figure 4.2 ERMs for the high homophone density condition (high HD, solid lines) and low homophone density condition (low HD, dashed lines) at the represented electrodes. 76
Figure 5.1 Topographic maps and averaged waveforms for four conditions over the selected sensors. White dots represent the sensors that showed significant effects of the interaction between homophone density (HD) and O-to-P consistency (OP) in the time windows (a) form 450 ms to 650 ms and (b) from 700 ms to 900 ms and a significant effect of HD in the time windows (c) from 700 ms to 900 ms. 86
Figure 5.2 The anatomical locations for regions of interest (ROIs) in the inflated (left) and pial (right) views of the automated anatomical parcellation (Destrieux et al., 2010). 88
Figure 5.3 The averaged dSPMs for each condition across all participants in the left hemispheres shown at 50 ms intervals from 300 ms to 1000 ms. 91

Figure 5.4 Bar plots of the averaged dSPMs within ROIs for the significant effects of (a) interaction between HD and O-to-P consistency (OP), (b) homophone density and (c) O-to-P consistency. 93
Figure 6.1 The interactive-activation framework for Chinese spoken words. The example of the monosyllabic Chinese spoken word cāng is used to illustrate the sublexical/lexical division imposed on orthographic and phonological representations. 100
Figure 6.2 (a) Results of the gPPI analysis regardless the conditions for the seed regions including the left pars opercularis (pOper), pars triangularis (pTri) and pars orbitalis (pOrb) of inferior frontal gyrus (IFG) in Experiments 1 and 2 (P < 0.0001, FWE-corrected, voxel size > 100). (b) Simplified illustration of the anatomy and connectivity of the left hemisphere language network (Hagoort, 2016). (c) The topographical connectivity pattern between frontal and temporal/parietal cortex in the perisylvian language networks (Hagoort, 2016; Xiang et al., 2010). 109

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