删除或更新信息,请邮件至freekaoyan#163.com(#换成@)

转换概率和词长期待对语音统计学习的影响

本站小编 Free考研考试/2022-01-01

于文勃1, 王璐1, 瞿邢芳1, 王天琳2, 张晶晶3, 梁丹丹1()
1南京师范大学文学院, 南京 210097
2纽约州立大学奥尔巴尼分校教育学院, 纽约 12222
3南京师范大学心理学院, 南京 210097
收稿日期:2020-07-11发布日期:2021-04-25
通讯作者:梁丹丹E-mail:ldd233@163.com

基金资助:江苏省社会科学基金项目研究成果(19YYC003);江苏高校优势学科建设工程资助项目(PAPD)

Transitional probabilities and expectation for word length impact verbal statistical learning

YU Wenbo1, WANG Lu1, QU Xingfang1, WANG Tianlin2, ZHANG Jingjing3, LIANG Dandan1()
1School of Chinese Language and Culture, Nanjing Normal University, Nanjing 210097, China
2School of Education, University at Albany, State University of New York, New York 12222, USA
3School of Psychology, Nanjing Normal University, Nanjing 210097, China
Received:2020-07-11Published:2021-04-25
Contact:LIANG Dandan E-mail:ldd233@163.com






摘要/Abstract


摘要: 语音统计学习指个体在加工人工语言过程中, 可以追踪音节间的转换概率实现切分语流、提取词(语)的过程。本研究采用2(转换概率:高转换概率、低转换概率) × 2(词长期待:两音节、三音节)的混合实验设计来考察转换概率和词长期待对语音统计学习的影响, 转换概率是被试间变量, 词长期待是被试内变量。事后检验发现, 仅在低转换概率人工语言的三音节迫选条件下, 被试没有表现出显著的学习效果。事先对比发现, 在学习低转换概率的人工语言后, 被试完成三音节迫选试次的成绩显著低于两音节迫选试次; 在三音节迫选试次中, 学习低转换概率人工语言被试的成绩也显著低于学习高转换概率被试的成绩。以上结果说明, 转换概率和词长期待共同影响个体语音统计学习的效果。



图1语音统计学习任务人工语言合成规则示意图
图1语音统计学习任务人工语言合成规则示意图


表1合成人工语言的音节、国际音标以及本实验中的目标词、非词
音节 音标 音节 音标 音节 音标 目标词 非词
nue nyɛ rua ʐua mei mei nueruote nuegeilai
ruo ʐuo dia tɪa rou ʐou liageirua liafote
te tʰɤ fo fo se diafolai diaruorua
lia lɪa lai laɪ remei rerou
gei kei re ʐɤ rouse meise

表1合成人工语言的音节、国际音标以及本实验中的目标词、非词
音节 音标 音节 音标 音节 音标 目标词 非词
nue nyɛ rua ʐua mei mei nueruote nuegeilai
ruo ʐuo dia tɪa rou ʐou liageirua liafote
te tʰɤ fo fo se diafolai diaruorua
lia lɪa lai laɪ remei rerou
gei kei re ʐɤ rouse meise



图2人工语言填充词示意图(上)和实验程序示意图(下)
图2人工语言填充词示意图(上)和实验程序示意图(下)



图3四种条件下被试迫选测验的正确率
图3四种条件下被试迫选测验的正确率


表2转换概率和词长期待对统计学习效果影响的方差分析结果
自变量 estimate SE t p
截距 0.59 0.01 41.04 < 0.001**
TP -0.02 0.01 -1.37 0.172
词长期待 0.02 0.01 1.42 0.157
TP×词长期待 0.02 0.01 1.22 0.222

表2转换概率和词长期待对统计学习效果影响的方差分析结果
自变量 estimate SE t p
截距 0.59 0.01 41.04 < 0.001**
TP -0.02 0.01 -1.37 0.172
词长期待 0.02 0.01 1.42 0.157
TP×词长期待 0.02 0.01 1.22 0.222


表3事先对比结果
自变量 estimate SE t p
截距 0.59 0.01 41.04 < 0.001**
对比1 -0.08 0.04 -2.05 0.041*
对比2 0.08 0.04 1.87 0.062
对比3 0.08 0.04 1.97 0.049*

表3事先对比结果
自变量 estimate SE t p
截距 0.59 0.01 41.04 < 0.001**
对比1 -0.08 0.04 -2.05 0.041*
对比2 0.08 0.04 1.87 0.062
对比3 0.08 0.04 1.97 0.049*







[1] Antovich, D.M., & Estes, K.G. (2017). Learning across languages: Bilingual experience supports dual language statistical word segmentation. Developmental Science, 21(2),e12548. https://doi.org/10.1111/desc.12548.
doi: 10.1111/desc.12548URL
[2] Arciuli, J., & Simpson, I.C. (2011). Statistical learning in typically developing children: The role of age and speed of stimulus presentation. Developmental Science, 14(3),464-473.
doi: 10.1111/desc.2011.14.issue-3URL
[3] Arciuli, J., & Simpson, I.C. (2012). Statistical learning is related to reading ability in children and adults. Cognitive science, 36(2),286-304.
doi: 10.1111/cogs.2012.36.issue-2URL
[4] Aslin, R.N., Saffran, J.R., & Newport, E.L. (1998). Computation of conditional probability statistics by 8-month-old infants. Psychological Science, 9(4),321-324.
doi: 10.1111/1467-9280.00063URL
[5] Batterink, L.J., Reber, P.J., Neville, H.J., & Paller, K.A. (2015). Implicit and explicit contributions to statistical learning. Journal of Memory and Language, 83,62-78.
pmid: 26034344
[6] Belliveau, J.W., Kennedy, D.N., Mckinstry, R.C., Buchbinder, B.R., Weisskoff, R.M., Cohen, M.S.,... Rosen, B.R. (1991). Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254(5032),716-719.
doi: 10.1126/science.1948051URL
[7] Bogaerts, L., Siegelman, N., & Frost, R. (2016). Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities. Psychonomic Bulletin & Review, 23(4),1250-1256.
doi: 10.3758/s13423-015-0996-zURL
[8] Bonatti, L.L., Pe?a, M., Nespor, M., & Mehler, J. (2005). Linguistic constraints on statistical computations: The role of consonants and vowels in continuous speech processing. Psychological Science, 16(6),451-459.
pmid: 15943671
[9] Bosseler, A.N., Teinonen, T., Tervaniemi, M., & Huotilainen, M. (2016). Infant directed speech enhances statistical learning in newborn infants: An ERP study. PLoS ONE, 11(9),e0162177. https://doi.org/10.1371/journal.pone.0162177.
doi: 10.1371/journal.pone.0162177URL
[10] Emberson, L.L., Misyak, J.B., Schwade, J.A., Christiansen, M.H., & Goldstein, M.H. (2019). Comparing statistical learning across perceptual modalities in infancy: An investigation of underlying learning mechanism (s). Developmental Science, 22(6),e12847. https://doi.org/10.1111/DESC.12847.
[11] Erickson, L.C., Thiessen, E.D., & Estes, K.G. (2014). Statistically coherent labels facilitate categorization in 8-month-olds. Journal of Memory and Language, 72,49-58.
doi: 10.1016/j.jml.2014.01.002URL
[12] Estes, K.G., Evans, J.L., Alibali, M.W., & Saffran, J.R. (2007). Can infants map meaning to newly segmented words? Statistical segmentation and word learning. Psychological Science, 18(3),254-260.
doi: 10.1111/j.1467-9280.2007.01885.xURL
[13] Estes, K.G., Gluck, S.C.W., & Bastos, C. (2015). Flexibility in statistical word segmentation: Finding words in foreign speech. Language Learning and Development, 11(3),252-269.
doi: 10.1080/15475441.2014.926730URL
[14] Estes, K.G., & Lew-Williams, C. (2015). Listening through voices: Infant statistical word segmentation across multiple speakers. Developmental Psychology, 51(11),1517-1528.
doi: 10.1037/a0039725URL
[15] Feng, S. (1996). Prosodic structures in Mandarin and its restrictions on syntactic structures. Linguistic Studies, (1),110-129.
[ 冯胜利. (1996). 论汉语的韵律结构及其对句法构造的制约. 语言研究>, (1),110-129.]
[16] Feng, S. (1998). On the "natural foot" of Chinese Mandarin. Studies of the Chinese Language, (1),40-47.
[ 冯胜利. (1998). 论汉语的“自然音步”. 中国语文>, (1),40-47.]
[17] Feng, Y.Q., Chu, M., He, L.,& Lv, S. N. (2001). Statistical analysis of syllable duration in Chinese discourse. Modern phonetics in the new century: Proceedings of the 5th National Conference on modern phonetics.
[ 冯勇强, 初敏, 贺琳, 吕士楠. (2001). 汉语话语音节时长统计分析. 新世纪的现代语音学——第五届全国现代语音学学术会议论文集>.]
[18] Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: SAGE. Publications Ltd.
[19] Frost, R., Armstrong, B.C., & Christiansen, M.H. (2020). Statistical learning research: A critical review and possible new directions. Psychological Bulletin, 145(12), 1128-1153.
doi: 10.1037/bul0000210URL
[20] Frost, R., Armstrong, B.C., Siegelman, N., & Christiansen, M.H. (2015). Domain generality versus modality specificity: The paradox of statistical learning. Trends in Cognitive Sciences, 19(3),117-125.
doi: 10.1016/j.tics.2014.12.010pmid: 25631249
[21] Frost, R.L.A., Monaghan, P., & Christiansen, M.H. (2019). Mark my words: High frequency marker words impact early stages of language learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 45(10),1883-1898.
doi: 10.1037/xlm0000683URL
[22] Gómez, D.M, Mok, P., Ordin, M., Mehler, J., & Nespor, M. (2017). Statistical speech segmentation in tone languages: The role of lexical tones. Language & Speech, 61(1),84-96.
[23] Harris, Z.S. (1954). Distributional structure. Word, 10(2-3),146-162.
[24] Harris, Z.S. (1955). From phoneme to morpheme. Language, 31(2),190-222.
doi: 10.2307/411036URL
[25] Hoch, L., Tyler, M.D., & Tillmann, B. (2013). Regularity of unit length boosts statistical learning in verbal and nonverbal artificial languages. Psychonomic Bulletin & Review, 20(1),142-147.
doi: 10.3758/s13423-012-0309-8URL
[26] Hudspeth, A.J. (1989). How the ear's works work. Nature, 341(6241),397-404.
pmid: 2677742
[27] Johnson, E.K., & Tyler, M.D. (2010). Testing the limits of statistical learning for word segmentation. Developmental Science, 13(2),339-345.
doi: 10.1111/desc.2010.13.issue-2URL
[28] Jones, M.R., & Boltz, M. (1989). Dynamic attending and responses to time. Psychological Review, 96(3),459-491.
pmid: 2756068
[29] Kidd, E., & Arciuli, J. (2016). Individual differences in statistical learning predict children's comprehension of syntax. Child Development, 87(1),184-193.
doi: 10.1111/cdev.12461URL
[30] Lew-Williams, C., & Saffran, J.R. (2012). All words are not created equal: Expectations about word length guide infant statistical learning. Cognition, 122(2),241-246.
doi: 10.1016/j.cognition.2011.10.007pmid: 22088408
[31] Li, B., & Liu, X. (2018). A quantitative analysis of Chinese vocabulary evolution based on The Great Chinese Dictionary. Journal of Nanjing Normal University (Social Science Edition), (5),152-160.
[ 李斌, 刘雪扬. (2018). 基于《汉语大词典》的汉语词汇历时演变计量研究. 南京师大学报(社会科学版)>, (05),152-160.]
[32] Mirman, D., Magnuson, J.S., Estes, K.G., & Dixon, J.A. (2008). The link between statistical segmentation and word learning in adults. Cognition, 108(1),271-280.
doi: 10.1016/j.cognition.2008.02.003URL
[33] Onnis, L., & Thiessen, E.D. (2013). Language experience changes subsequent learning. Cognition, 126(2),268-284.
doi: 10.1016/j.cognition.2012.10.008pmid: 23200510
[34] Palmer, S.D., Hutson, J., White, L., & Mattys, S.L. (2019). Lexical knowledge boosts statistically-driven speech segmentation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(1),139-146.
[35] Palmer, S.D., & Mattys, S.L. (2016). Speech segmentation by statistical learning is supported by domain-general processes within working memory. The Quarterly Journal of Experimental Psychology, 69(12),2390-2401.
doi: 10.1080/17470218.2015.1112825URL
[36] Potter, C.E., Wang, T., & Saffran, J.R. (2017). Second language experience facilitates statistical learning of novel linguistic materials. Cognitive Science, 41(S4),913-927.
doi: 10.1111/cogs.2017.41.issue-S4URL
[37] Poulin-Charronnat, B., Perruchet, P., Tillmann, B., & Peereman, R. (2016). Familiar units prevail over statistical cues in word segmentation. Psychological Research, 81,990-1003.
doi: 10.1007/s00426-016-0793-yURL
[38] Raviv, L., & Arnon, I. (2018). The developmental trajectory of children's auditory and visual statistical learning abilities: Modality-based differences in the effect of age. Developmental Science, 21(4),e12593. https://doi.org/10.1111/desc.12593.
doi: 10.1111/desc.2018.21.issue-4URL
[39] Revelle, W.R. (2016). psych: procedures for personality and psychological research. R package version 1.6.6. Available at: http://cran.r-project.org/package=psych.
[40] Saffran, J.R., Aslin, R.N., & Newport, E.L. (1996). Statistical learning by 8-month-old infants. Science, 274(5294),1926-1928.
doi: 10.1126/science.274.5294.1926URL
[41] Saffran, J.R., & Kirkham, N.Z. (2018). Infant statistical learning. Annual Review of Psychology, 69(1),181-203.
doi: 10.1146/annurev-psych-122216-011805URL
[42] Saksida, A., Langus, A., & Nespor, M. (2017). Co-occurrence statistics as a language-dependent cue for speech segmentation. Developmental Science, 20(3),e12390. https://doi.org/10.1111/desc.12390.
doi: 10.1111/desc.2017.20.issue-3URL
[43] Schad, D.J., Vasishth, S., Hohenstein, S., & Kliegl, R. (2020). How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language, 110,104038. https:doi.org/10.1016/j.jml.2019.104038.
doi: 10.1016/j.jml.2019.104038URL
[44] Shoaib, A., Wang, T., Hay, J.F., & Lany, J. (2018). Do infants learn words from statistics? Evidence from English-learning infants hearing Italian. Cognitive Science, 42(8),3083-3099.
doi: 10.1111/cogs.12673URL
[45] Shu, H., & Zhang, Y.X. (2008). Research methods in psychology: Experimental design and data analysis. Beijing,China: People's Education Press.
[ 舒华, 张亚旭. (2008). 心理学研究方法: 实验设计和数据分析>. 北京: 人民教育出版社.]
[46] Song, Y.N.& He, W. (2005). Statistical analysis of monosyllabic duration in standard Mandarin. National Conference on man machine voice communication, Statistical analysis of monosyllabic duration in standard Mandarin. National Conference on man machine voice communication.
[ 宋雅男, 何伟. (2005). 标准普通话单音节时长统计分析. 全国人机语音通讯学术会议>. ]
[47] Siegelman, N., Bogaerts, L., Christiansen, M.H., & Frost, R. (2017). Towards a theory of individual differences in statistical learning. Philosophical Transactions of the Royal Society B, 372(1711). https://doi.org/10.1098/rstb.2016.0059.
[48] Siegelman, N., Bogaerts, L., Elazar, A., Arciuli, J., & Frost, R. (2018). Linguistic entrenchment: Prior knowledge impacts statistical learning performance. Cognition, 177,198-213.
doi: S0010-0277(18)30104-5pmid: 29705523
[49] Siegelman, N., Bogaerts, L., & Frost, R. (2017). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. Behavior Research Methods, 49(2),418-432.
doi: 10.3758/s13428-016-0719-zpmid: 26944577
[50] Siegelman, N., Bogaerts, L., Kronenfeld, O., & Frost, R. (2018). Redefining "learning" in statistical learning: What does an online measure reveal about the assimilation of visual regularities? Cognitive Science, 42(S3),692-727.
doi: 10.1111/cogs.2018.42.issue-S3URL
[51] Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: Theoretical perspectives and empirical evidence. Journal of Memory and Language, 81,105-120.
pmid: 25821343
[52] Thiessen, E.D., Hill, E.A., & Saffran, J.R. (2005). Infant-directed speech facilitates word segmentation. Infancy, 7(1),53-71.
doi: 10.1207/s15327078in0701_5URL
[53] Thiessen, E.D., & Saffran, J.R. (2003). When cues collide: Use of stress and statistical cues to word boundaries by 7- to 9-month-old infants. Developmental Psychology, 39(4),706-716.
pmid: 12859124
[54] Toro, J.M., Pons, F., Bion, R.A.H., & Sebastián-Gallés, N. (2011). The contribution of language-specific knowledge in the selection of statistically-coherent word candidates. Journal of Memory and Language, 64(2),171-180.
doi: 10.1016/j.jml.2010.11.005URL
[55] Toro, J.M., Sinnett, S., & Sotofaraco, S. (2005). Speech segmentation by statistical learning depends on attention. Cognition, 97(2),B25-B34. https://doi.org/10.1016/j.cognition.2005.01.006.
[56] Wang, T.L., & Saffran, J.R. (2014). Statistical learning of a tonal language: The influence of bilingualism and previous linguistic experience. Frontiers in Psychology, 5(324),953. https://doi.org/10.3389/fpsyg.2014.00953.
[57] Yu, W., & Liang, D. (2018). Word segmentation cues in the process of spoken language. Advances in Psychological Science, 26(10),1765-1774.
doi: 10.3724/SP.J.1042.2018.01765URL
[ 于文勃, 梁丹丹. (2018). 口语加工中的词语切分线索. 心理科学进展>, 26(10),1765-1774.]
[58] Yu, W., Wang, L., Cheng, X., Wang, T., Zhang, J., & Liang, D. (2021). The influence of linguistic experience on statistical word segmentation. Advances in Psychological Science, 29(6),787-795.
doi: 10.3724/SP.J.1042.2021.00787URL
[ 于文勃, 王璐, 程幸悦, 王天琳, 张晶晶, 梁丹丹. (2021). 语言经验对概率词切分的影响. 心理科学进展>, 29(6),787-795.]
[59] Zhang, H., Sun, H., & Gu, J. (2013). The Interaction between prosody and syntax in Chinese idiom processing: An event-related potentials investigation. Journal of Foreign Languages, (1),22-31.
[ 张辉, 孙和涛, 顾介鑫. (2013). 成语加工中韵律与句法互动的事件相关电位研究. 外国语: 上海外国语大学学报>, (1),22-31.]




[1]雷震, 毕蓉, 莫李澄, 于文汶, 张丹丹. 外显和内隐情绪韵律加工的脑机制:近红外成像研究[J]. 心理学报, 2021, 53(1): 15-25.
[2]张政华, 韩梅, 张放, 李卫君. 音乐训练促进诗句韵律整合加工的神经过程[J]. 心理学报, 2020, 52(7): 847-860.
[3]胡金生, 李骋诗, 王琦, 李松泽, 李涛涛, 刘淑清. 孤独症青少年的情绪韵律注意偏向缺陷:低效率的知觉模式[J]. 心理学报, 2018, 50(6): 637-646.
[4]李卫君, 刘梦, 张政华, 邓娜丽, 邢钰珊. 口吃者加工汉语歧义短语的神经过程[J]. 心理学报, 2018, 50(12): 1323-1335.
[5]郑志伟;黄贤军;张钦. 情绪韵律调节情绪词识别的ERP研究[J]. 心理学报, 2013, 45(4): 427-437.
[6]王异芳;苏彦捷;何曲枝. 3~5岁儿童基于声音线索的情绪知觉[J]. 心理学报, 2012, 44(11): 1472-1478.
[7]李卫君,杨玉芳. 绝句韵律边界的认知加工及其脑电效应[J]. 心理学报, 2010, 42(11): 1021-1032.
[8]覃薇薇,刘思耘,杨莉,周宗奎. 前分类声音存储器对声调和情绪韵律的加工[J]. 心理学报, 2010, 42(06): 651-662.
[9]郑波,王蓓,杨玉芳. 韵律对指代歧义的解歧作用及其机制[J]. 心理学报, 2002, 34(06): 15-20.
[10]仲晓波,王蓓,杨玉芳,吕士楠. 普通话韵律词重音知觉[J]. 心理学报, 2001, 33(6): 2-9.
[11]仲晓波,杨玉芳. 国外关于韵律特征和重音的一些研究[J]. 心理学报, 1999, 31(4): 468-475.
[12]杨玉芳. 词切分的韵律学线索[J]. 心理学报, 1992, 24(4): 59-65.





PDF全文下载地址:

http://journal.psych.ac.cn/xlxb/CN/article/downloadArticleFile.do?attachType=PDF&id=4953
相关话题/概率 语言 心理 统计 实验