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研究生: 林耿育
Lin, Keng-Yu
論文名稱: 語意特徵於中文基礎詞彙處理扮演之角色:事件相關腦電位研究
The Role of Semantic Features in Processing Basic-Level Concepts in Mandarin Chinese: An ERP Study
指導教授: 詹曉蕙
Chan, Shiao-Hui
學位類別: 碩士
Master
系所名稱: 英語學系
Department of English
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 87
中文關鍵詞: 中文概念知識事件相關腦電位語意促發感知功能理論
英文關鍵詞: priming paradigm, sensory-functional theory
論文種類: 學術論文
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  • 概念的理解與處理一直以來都是個熱門的議題。過去的研究指出,人們在處理自然物的概念時,其感官語意特徵比功能語意特徵來得重要,而在處理人造物的概念時,其功能語意特徵比感官語意特徵來得重要。本文旨在以事件相關腦電位的技術,探討是否前人的發現是來自於語意特徵與語意類別之間的關係遠近。。本實驗招募中文母語人士為受試者,以語意促發作業(semantic priming)與延遲反應(delayed-response task)為設計,請其判斷實驗促發詞(prime,包括感官特徵與功能特徵)是否可以是實驗目標詞(target,包括自然物與人造物)的一個特點。實驗結果發現,在控制了促發詞與目標詞的關聯度後,在N1腦波和N400腦波裡,不同的語意特徵並不會和不同的語意類別產生交互作用,但在LPC腦波裡,不同的語意特徵和不同的語意類別則有交互作用。進一步檢定發現,此交互作用發生在人造物的概念上。根據實驗結果,本研究推斷不同的語意特徵在處理不同的語意類別時,可能有著不同的重要性。而從腦波的時間點來看,不同語意特徵對不同語意類別影響可能為控制歷程(controlled process)而非自動化歷程(automatic process)。

    This study investigated the role of semantic features in processing basic-level concepts in Mandarin Chinese with the event-related potential (ERP) technique. In previous studies, different semantic features were claimed to have different prominence over different semantic categories (Warrington & McCarthy, 1983; Warrington & Shallice, 1984). To examine whether the observed relationship between semantic features and semantic categories was determined by the relatedness between them, the present study adopted a priming paradigm, with the experimental stimulus being a perceptual or functional feature followed by a natural category or artifact. The relatedness between the features and categories were carefully controlled and the subjects’ task was to judge whether the prime could be one characteristic of the target with a delayed response. The results showed that different semantic categories did not interact with different semantic features in early time windows, N1 (100 ms – 150 ms) and N400 (300 ms – 400 ms), but did so in a later time window, LPC (500 ms – 700 ms) in artifacts. This finding partly supported the sensory-functional theory that different semantic features have different prominence over different semantic categories. Since the interaction was found to exist in artifacts in the present study, it was argued that the prominence of perceptual/functional features over natural categories/artifacts might not be an automatic process but a controlled one.

    Table of Contents 摘要 -------------------------------------------------------------------------------------------------- i Abstract ----------------------------------------------------------------------------------------------- ii Acknowledgements --------------------------------------------------------------------------------- iii Table of Contents ------------------------------------------------------------------------------------ vii List of Tables ----------------------------------------------------------------------------------------- ix List of Figures ---------------------------------------------------------------------------------------- x Chapter 1 Introduction ------------------------------------------------------------------------------ 1 1.1 Motivation and Research Questions ---------------------------------------------------- 1 1.2 Significance of the Current Study ------------------------------------------------------ 3 Chapter 2 Literature Review ----------------------------------------------------------------------- 5 2.1 Theories of Conceptual Structure ------------------------------------------------------- 5 2.1.1 Network Models ------------------------------------------------------------------ 5 2.1.2 Sensory-Functional Theory ----------------------------------------------------- 6 2.1.3 Parallel Distributed Processing Model (PDP Model) ------------------------ 8 2.1.4 Domain-Specific Knowledge --------------------------------------------------- 9 2.1.5 Limitations of the Theories ------------------------------------------------------ 10 2.2 The Role of Relatedness in Association with the Theories -------------------------- 13 2.3 The Event-Related Brain Potential Components -------------------------------------- 15 2.3.1 N1 Component -------------------------------------------------------------------- 15 2.3.2 N400 Component ----------------------------------------------------------------- 18 2.3.3 LPC Component ------------------------------------------------------------------ 22 2.4 Summary of the Literature Review ----------------------------------------------------- 23 Chapter 3 Methodology ----------------------------------------------------------------------------- 25 3.1 Participants --------------------------------------------------------------------------------- 25 3.2 Materials ------------------------------------------------------------------------------------ 26 3.3 Procedure ----------------------------------------------------------------------------------- 29 3.4 Behavioral and EEG Recordings -------------------------------------------------------- 31 3.5 Data Analysis ------------------------------------------------------------------------------ 32 Chapter 4 Results ------------------------------------------------------------------------------------ 36 4.1 Behavioral Data --------------------------------------------------------------------------- 36 4.2 ERP Data ----------------------------------------------------------------------------------- 36 4.2.1 Mean Amplitude ------------------------------------------------------------------ 37 4.2.1.1 N1 -------------------------------------------------------------------------- 37 4.2.1.2 N400 ----------------------------------------------------------------------- 38 4.2.1.3 LPC ------------------------------------------------------------------------ 39 4.2.2 Fractional Area Latency --------------------------------------------------------- 41 4.2.2.1 N400 ------------------------------------------------------------------------ 41 4.2.2.2 LPC ------------------------------------------------------------------------- 44 4.3 Interim Summary of Results ------------------------------------------------------------- 47 Chapter 5 Discussion -------------------------------------------------------------------------------- 49 Chapter 6 Conclusion ------------------------------------------------------------------------------- 54 6.1 Summary of the Current Study ---------------------------------------------------------- 54 6.2 Limitations and Future Direction -------------------------------------------------------- 55 References --------------------------------------------------------------------------------------------- 57 Appendix I. Experimental Stimuli ----------------------------------------------------------------- 63 Appendix II. The Questionnaires of the Production of Semantic Features ------------------- 65 Appendix III. The Frequency of the Experimental Stimuli ------------------------------------- 74 Appendix IV. The Questionnaires of the Familiarity Rating ----------------------------------- 76 Appendix V. The Values of the Familiarity Rating of the Experimental Stimuli ------------ 83 Appendix VI. The Values of the Relatedness Rating of the Experimental Stimuli ---------- 85

    Amsel, B. D. (2011). Tracking real-time neural activation of conceptual knowledge using single-trial event-related potentials. Neuropsychologia, 49(5), 970-983.
    Bloom, P. (1996). Intention, history, and artifact concepts. Cognition, 60(1), 1-29.
    Caramazza, A., & Mahon, B. Z. (2003). The organization of conceptual knowledge: the evidence from category-specific semantic deficits. Trends in cognitive sciences, 7(8), 354-361.
    Caramazza, A., & Shelton, J. (1998). Domain-specific knowledge systems in the brain: The animate-inanimate distinction. Cognitive Neuroscience, Journal of, 10(1), 1-34.
    Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological review, 82(6), 407.
    Corbeil, J.-C., & Archambault, A. (1997). The Macmillan visual dictionary: Webster's New World.
    Cree, G. S., McNorgan, C., & McRae, K. (2006). Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(4), 643.
    Crutch, S. J., & Warrington, E. K. (2003). The selective impairment of fruit and vegetable knowledge: amultiple processing channels account of fine-grain category specificity. Cognitive Neuropsychology, 20(3-6), 355-372.
    Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21.
    Dunn, B. R., Dunn, D. A., Languis, M., & Andrews, D. (1998). The relation of ERP components to complex memory processing. Brain and cognition, 36(3), 355-376.
    Farah, M. J., & McClelland, J. L. (1991). A computational model of semantic memory impairment: modality specificity and emergent category specificity. Journal of Experimental Psychology: General, 120(4), 339.
    Federmeier, K. D., Segal, J. B., Lombrozo, T., & Kutas, M. (2000). Brain responses to nouns, verbs and class-ambiguous words in context. Brain, 123(12), 2552-2566.
    Harbin, T. J., Marsh, G. R., & Harvey, M. T. (1984). Differences in the late components of the event-related potential due to age and to semantic and non-semantic tasks. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 59(6), 489-496.
    Hill, H., Strube, M., Roesch-Ely, D., & Weisbrod, M. (2002). Automatic vs. controlled processes in semantic priming—differentiation by event-related potentials. International Journal of Psychophysiology, 44(3), 197-218.
    Hillyard, S. A., Vogel, E. K., & Luck, S. J. (1998). Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 353(1373), 1257-1270.
    Hoenig, K., Sim, E., Bochev, V., Herrnberger, B., & Kiefer, M. (2008). Conceptual flexibility in the human brain: dynamic recruitment of semantic maps from visual, motor, and motion-related areas. Cognitive Neuroscience, Journal of, 20(10), 1799-1814.
    Hollan, J. D. (1975). Features and semantic memory: Set-theoretic or network model?
    Jakić, M., Filipović-Đurđević, D., & Kostić, A. (2011). The facilitation effect of associative and semantic relatedness in word recognition. Psihologija, 44(4), 367-385.
    Kellenbach, M. L., Brett, M., & Patterson, K. (2001). Large, colorful, or noisy? Attribute-and modality-specific activations during retrieval of perceptual attribute knowledge. Cognitive, Affective, & Behavioral Neuroscience, 1(3), 207-221.
    Kellenbach, M. L., Wijers, A. A., & Mulder, G. (2000). Visual semantic features are activated during the processing of concrete words: Event-related potential evidence for perceptual semantic priming. Cognitive Brain Research, 10(1), 67-75.
    Kiefer, M. (2001). Perceptual and semantic sources of category-specific effects: event-related potentials during picture and word categorization. Memory & Cognition, 29(1), 100-116.
    Kiefer, M. (2002). The N400 is modulated by unconsciously perceived masked words: Further evidence for an automatic spreading activation account of N400 priming effects. Cognitive Brain Research, 13(1), 27-39.
    Kiefer, M., & Pulvermüller, F. (2012). Conceptual representations in mind and brain: theoretical developments, current evidence and future directions. Cortex, 48(7), 805-825.
    Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP). Annual review of psychology, 62, 621-647.
    Liotti, M., Woldorff, M. G., Perez, R., & Mayberg, H. S. (2000). An ERP study of the temporal course of the Stroop color-word interference effect. Neuropsychologia, 38(5), 701-711.
    Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in human neuroscience, 8.
    Luck, S. J. (2014). An introduction to the event-related potential technique: MIT press.
    Luck, S. J., Heinze, H., Mangun, G., & Hillyard, S. A. (1990). Visual event-related potentials index focused attention within bilateral stimulus arrays. II. Functional dissociation of P1 and N1 components. Electroencephalography and clinical neurophysiology, 75(6), 528-542.
    Mangun, G. R., & Hillyard, S. A. (1991). Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming. Journal of Experimental Psychology: Human Perception and Performance, 17(4), 1057.
    Oldfield, R. C. (1971). The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia, 9(1), 97-113.
    Pietrini, V., Nertempi, P., Vaglia, A., Revello, M., Pinna, V., & Ferro-Milone, F. (1988). Recovery from herpes simplex encephalitis: selective impairment of specific semantic categories with neuroradiological correlation. Journal of Neurology, Neurosurgery & Psychiatry, 51(10), 1284-1293.
    Rips, L. J., Shoben, E. J., & Smith, E. E. (1973). Semantic distance and the verification of semantic relations. Journal of verbal learning and verbal behavior, 12(1), 1-20.
    Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive psychology, 8(3), 382-439.
    Santos, A. T., Marques, J. F., & Correia, L. (2014). A Computational Model of Semantic Memory Categorization: Identification of a Concept’s Semantic Level from Feature Sharedness. Cognitive Computation, 6(2), 175-181.
    Sim, E. J., & Kiefer, M. (2005). Category-related brain activity to natural categories is associated with the retrieval of visual features: Evidence from repetition effects during visual and functional judgments. Cognitive Brain Research, 24(2), 260-273.
    Smith, E. E., Shoben, E. J., & Rips, L. J. (1974). Structure and process in semantic memory: A featural model for semantic decisions. Psychological review, 81(3), 214.
    Tanaka, J., Luu, P., Weisbrod, M., & Kiefer, M. (1999). Tracking the time course of object categorization using event-related potentials. NeuroReport, 10(4), 829-835.
    Turnbull IV, O. H., & Laws, K. R. (2000). Loss of stored knowledge of object structure: Implications for “category-specific” deficits. Cognitive Neuropsychology, 17(4), 365-389.
    Tyler, L. K., Moss, H., Durrant-Peatfield, M., & Levy, J. (2000). Conceptual structure and the structure of concepts: A distributed account of category-specific deficits. Brain and language, 75(2), 195-231.
    Warrington, E. K., & McCarthy, R. (1983). Category specific access dysphasia. Brain, 106(4), 859-878.
    Warrington, E. K., & McCarthy, R. A. (1987). Categories of knowledge further fractionations and an attempted integration. Brain, 110(5), 1273-1296.
    Warrington, E. K., & Shallice, T. (1984). Category specific semantic impairments. Brain, 107(3), 829-853.

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