Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2016, Vol. 56 Issue (9): 942-948    DOI: 10.16511/j.cnki.qhdxxb.2016.21.050
  电子工程 本期目录 | 过刊浏览 | 高级检索 |
中国手语模态对句子加工的影响
姚登峰1,2,3, 江铭虎1,2, 阿布都克力木·阿布力孜1,2
1. 清华大学 人文学院计算语言学实验室, 北京 100084;
2. 清华大学 心理学与认知科学研究中心, 北京 100084;
3. 北京市信息服务工程重点实验室(北京联合大学), 北京 100075
Effects of Chinese sign language modality on processing sentences
YAO Dengfeng1,2,3, JIANG Minghu1,2, ABUDOUKELIMU Abulizi1,2
1. Lab of Computational Linguistics, School of Humanities, Tsinghua University, Beijing 10084, China;
2. Center for Psychology and Cognitive Science, Tsinghua University, Beijing 10084, China;
3. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
全文: PDF(1521 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 该文系统地研究了中国手语模态对句子加工的影响,使用事件相关电位(ERP)技术来探索句子加工过程中手势模态的语义和语音信息对词汇通达的影响和时间过程。设计实验材料时,对句尾变化条件下目标手势的语义和音韵形式进行了系统的人为操纵。通过18个被试的实验,观察到将初级视觉特征映射到手势音韵特征的手势感知证据(时长100~200ms),紧接着是将目标手势音韵特征与语境信息匹配的负成分(时长200~400ms),以及表征语篇语境对语义制约的动态建构的负成分(时长400~650ms),它促进了手势的音义信息与句子信息的整合。音近和义近手势在时间进程和效应方向上存在着相似性,表明手语模态的词汇通达与有声语言类似,并消耗类似的在线处理成本。同时发现了手语模态独有的表征手语音韵特征加工已经完成的N300效应,它代表了手势音韵元素识别和手势识别之间的直接联系。实验结果表明:在进行句子加工时,被试首要考虑的是语境和目标手势匹配,并不是与手势的音韵特征匹配。这种结果可能是词汇选择时音近和义近相关信息相互作用的标志。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
关键词 手语语音语义句子加工N300    
Abstract:The effects of Chinese sign language (CSL) modality on processing sentences are investigated by using the event-related potential method to focus on the influence of both semantic and phonological information on the lexical accessment during sentence processing and its timing. The semantic expectancy and phonetic form of the target signs with the final inflections are manipulated during stimuli selection in the test design. The electroencephalogram data of 18 native CSL signers were recorded and analyzed. The sign perception evidence (100-200 ms) reflects the process of mapping the primary visual features into the phonological features of the signs. This was followed by two negativities associated with matching the phonological features of the target sign with contextual information (200-400 ms) and the dynamic construction of the semantic restriction of the discourse context (400-650 ms) that promoted integration of the lexicon and sentence context. The phonological and semantic-related signs shared a similar timing pattern and the effect direction verified the similarity of the semantic and phonological properties access and the similar online processing cost of CSL and ordinary Chinese sentence comprehension. An N300 effect was found that uniquely indicates the successful processing of phonetic features in the sign language modality. This effect represents the direct link between the phonological feature recognition and the sign recognition. The results show that the subject considers the match between context and target sign as a priority, rather than the phonological features of the signs. This result indicates the interaction between the phonological and semantic-related information at the moment of lexical selection.
Key wordssign language    phonology    meaning    sentence processing    N300
收稿日期: 2015-12-04      出版日期: 2016-09-15
ZTFLH:  B849  
通讯作者: 江铭虎,教授,E-mail:jiang.mh@tsinghua.edu.cn     E-mail: jiang.mh@tsinghua.edu.cn
引用本文:   
姚登峰, 江铭虎, 阿布都克力木·阿布力孜. 中国手语模态对句子加工的影响[J]. 清华大学学报(自然科学版), 2016, 56(9): 942-948.
YAO Dengfeng, JIANG Minghu, ABUDOUKELIMU Abulizi. Effects of Chinese sign language modality on processing sentences. Journal of Tsinghua University(Science and Technology), 2016, 56(9): 942-948.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.21.050  或          http://jst.tsinghuajournals.com/CN/Y2016/V56/I9/942
  表1 实验材料实例
  图1 实验视频
  图2 四种控制启动条件下F4的ERP平均波形图
[1] Kutas M, Hillyard S A. Reading senseless sentences: Brain potentials reflect semantic incongruity [J]. Science, 1980, 207(4427): 203-205.
[2] Friederici A D. Towards a neural basis of auditory sentence processing [J]. Trends in Cognitive Sciences, 2002, 6(2): 78-84.
[3] Kolk H H J, Chwilla D J, Van Herten M, et al. Structure and limited capacity in verbal working memory: A study with event-related potentials [J]. Brain and language, 2003, 85(1): 1-36.
[4] Kuperberg G R. Neural mechanisms of language comprehension: Challenges to syntax [J]. Brain Research, 2007, 1146: 23-49.
[5] Bornkessel I, Schlesewsky M. The extended argument dependency model: a neurocognitive approach to sentence comprehension across languages [J]. Psychological Review, 2006, 113(4): 787.
[6] Brouwer H, Fitz H, Hoeks J. Getting real about semantic illusions: rethinking the functional role of the P600 in language comprehension [J]. Brain Research, 2012, 1446: 127-143.
[7] Stevens K N, Blumstein S E. The search for invariant acoustic correlates of phonetic features [J]. Perspectives on the Study of Speech, 1981: 1-38.
[8] Stokoe W C. Sign language structure: An outline of the visual communication systems of the American deaf [J]. Journal of deaf studies and deaf education, 2005, 10(1): 3-37.
[9] Morgan G, Barrett-Jones S, Stoneham H. The first signs of language: Phonological development in British Sign Language [J]. Applied Psycholinguistics, 2007, 28(01): 3-22.
[10] Corina D P, Hildebrandt U C. Psycholinguistic investigations of phonological structure in ASL [J]. Modality and Structure in Signed and Spoken Languages, 2002: 88-111.
[11] Carreiras M, Gutiérrez-Sigut E, Baquero S, et al. Lexical processing in Spanish sign language (LSE) [J]. Journal of Memory and Language, 2008, 58(1): 100-122.
[12] Dufour S, Peereman R. Inhibitory priming effects in auditory word recognition: When the target's competitors conflict with the prime word [J]. Cognition, 2003, 88(3): B33-B44.
[13] Gutierrez E, Williams D, Grosvald M, et al. Lexical access in American Sign Language: An ERP investigation of effects of semantics and phonology [J]. Brain Research, 2012, 1468: 63-83.
[14] Neville H J, Coffey S A, Lawson D S, et al. Neural systems mediating American Sign Language: Effects of sensory experience and age of acquisition [J]. Brain and Language, 1997, 57(3): 285-308.
[15] Eimer M. Does the face-specific N170 component reflect the activity of a specialized eye processor? [J]. Neuroreport, 1998, 9(13): 2945-2948.
[16] Eimer M. The face-specific N170 component reflects late stages in the structural encoding of faces [J]. Neuroreport, 2000, 11(10): 2319-2324. -2324.
[17] Chauncey K, Holcomb P J, Grainger J. Effects of stimulus font and size on masked repetition priming: An event-related potentials (ERP) investigation [J]. Language and Cognitive Processes, 2008, 23(1): 183-200.
[18] Emmorey K, Corina D. Lexical recognition in sign language: Effects of phonetic structure and morphology [J]. Perceptual and Motor Skills, 1990, 71(3f): 1227-1252.
[19] Dambacher M, Rolfs M, Giillner K, et al. Event-related potentials reveal rapid verification of predicted visual input [J]. PLoS ONE, 2009, 4(3): e5047.
[20] Kim A, Lai V. Rapid interactions between lexical semantic and word form analysis during word recognition in context: Evidence from ERPs [J]. Journal of Cognitive Neuroscience, 2012, 24(5): 1104-1112.
[21] Emmorey K, Damasio H, McCullough S, et al. Neural systems underlying spatial language in American Sign Language [J]. Neuroimage, 2002, 17(2): 812-824.
[22] Kutas M, Neville H J, Holcomb P J. A preliminary comparison of the N400 response to semantic anomalies during reading, listening and signing [J]. Electroencephalography and Clinical Neurophysiology Supplement, 1987, 39: 325-330.
[23] Colin C, Zuinen T, Bayard C, et al. Phonological processing of rhyme in spoken language and location in sign language by deaf and hearing participants: A neurophysiological study [J]. Neurophysiologie Clinique/Clinical Neurophysiology, 2013, 43(3): 151-160.
[24] Barrett S E, Rugg M D. Event-related potentials and the semantic matching of pictures [J]. Brain and cognition, 1990, 14(2): 201-212.
[25] McPherson W B, Holcomb P J. An electrophysiological investigation of semantic priming with pictures of real objects [J]. Psychophysiology, 1999, 36(01): 53-65.
[26] Luck S J. An introduction to the event-related potential technique [M]. MIT press, 2014.
[27] Holcomb P J, Grainger J, O'rourke T. An electrophysiological study of the effects of orthographic neighborhood size on printed word perception [J]. Journal of Cognitive Neuroscience, 2002, 14(6): 938-950.
[28] Massol S, Grainger J, Dufau S, et al. Masked priming from orthographic neighbors: An ERP investigation [J]. Journal of Experimental Psychology: Human Perception and Performance, 2010, 36(1): 162-174.
[29] Diependaele K, Ziegler J C, Grainger J. Fast phonology and the bimodal interactive activation model [J]. European Journal of Cognitive Psychology, 2010, 22(5): 764-778.
[30] Niznikiewicz M, Squires N K. Phonological processing and the role of strategy in silent reading: behavioral and electrophysiological evidence [J]. Brain and Language, 1996, 52(2): 342-364.
[31] McClelland J L, Rumelhart D E. An interactive activation model of context effects in letter perception: I. An account of basic findings [J]. Psychological Review, 1981, 88(5): 375-407.
[1] 陈波, 张华, 陈永灿, 李永龙, 熊劲松. 基于特征增强的水工结构裂缝语义分割方法[J]. 清华大学学报(自然科学版), 2023, 63(7): 1135-1143.
[2] 黄玥诚, 张桎淮, 曹思涵, 李建华, 方东平. 基于语义分析的建筑业安全文化管理机制设计[J]. 清华大学学报(自然科学版), 2023, 63(2): 179-190.
[3] 逯波, 段晓东, 袁野. 面向跨模态检索的自监督深度语义保持Hash[J]. 清华大学学报(自然科学版), 2022, 62(9): 1442-1449.
[4] 杨思琴, 江铭虎. 汉语[S+V+O]简单句式的语义和句法加工研究——ERPs实验证据[J]. 清华大学学报(自然科学版), 2022, 62(12): 2053-2060.
[5] 侯文惠, 曲维光, 魏庭新, 李斌, 顾彦慧, 周俊生. 面向中文AMR标注体系的兼语语料库构建及兼语结构识别[J]. 清华大学学报(自然科学版), 2021, 61(9): 920-926.
[6] 满志博, 毛存礼, 余正涛, 李训宇, 高盛祥, 朱俊国. 基于多语言联合训练的汉-英-缅神经机器翻译方法[J]. 清华大学学报(自然科学版), 2021, 61(9): 927-935.
[7] 曹来成, 吴琪瑞, 王娅菲, 吴蓉, 郭显. 基于语义的多用户高效搜索方案[J]. 清华大学学报(自然科学版), 2021, 61(11): 1228-1233.
[8] 宫琴, 饶诚, 郑硕. 抑制多方向语音噪声的人工耳蜗语音增强算法[J]. 清华大学学报(自然科学版), 2020, 60(2): 181-188.
[9] 夏吾吉, 华却才让. 基于依存树的藏语语义分析[J]. 清华大学学报(自然科学版), 2019, 59(9): 750-756.
[10] 骆歆远, 陈欣, 寿黎但, 陈珂, 吴妍静. 面向室内空间的语义轨迹提取框架[J]. 清华大学学报(自然科学版), 2019, 59(3): 186-193.
[11] 宫琴, 郑硕. 基于波束形成与最大似然估计的近距离双麦克风语音增强算法[J]. 清华大学学报(自然科学版), 2018, 58(6): 603-608.
[12] 张雪英, 牛溥华, 高帆. 基于DNN-LSTM的VAD算法[J]. 清华大学学报(自然科学版), 2018, 58(5): 509-515.
[13] 路文焕, 冯晓艳, HONDA Kiyoshi, 魏建国. 基于MRI研究相对舌体大小对个性化发音的影响[J]. 清华大学学报(自然科学版), 2018, 58(4): 357-361.
[14] 吕学强, 张学敬, 周强. 对话语篇中对话者的心理距离预测初探[J]. 清华大学学报(自然科学版), 2018, 58(4): 362-367.
[15] 张宇, 张鹏远, 颜永红. 基于注意力LSTM和多任务学习的远场语音识别[J]. 清华大学学报(自然科学版), 2018, 58(3): 249-253.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn