ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

心理科学进展 ›› 2021, Vol. 29 ›› Issue (5): 787-795.doi: 10.3724/SP.J.1042.2021.00787

• 研究前沿 • 上一篇    下一篇

语言经验对概率词切分的影响

于文勃1, 王璐1, 程幸悦1, 王天琳2, 张晶晶3, 梁丹丹1()   

  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:2020-06-28 出版日期:2021-05-15 发布日期:2021-03-30
  • 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成果(19YYC003);江苏高校优势学科建设工程资助项目(PAPD)

The influence of linguistic experience on statistical word segmentation

YU Wenbo1, WANG Lu1, CHENG Xingyue1, WANG Tianlin2, ZHANG Jingjing3, LIANG Dandan1()   

  1. 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:2020-06-28 Online:2021-05-15 Published:2021-03-30
  • Contact: LIANG Dandan E-mail:ldd233@163.com

摘要:

概率词切分指个体利用音节间的转换概率切分语流、发现词语边界的过程。经典的概率词切分研究多采用“学习-测试”范式, 首先要求被试切分一段无意义人工语言, 随后对切分效果进行测试。近年来, 研究者逐渐关注语言经验对概率词切分的影响, 具体包括语音经验和被试掌握的语言知识两方面。今后的研究, 一方面可以更多地关注普通话母语者的语言经验如何作用于概率词切分过程; 另一方面还可以在语言经验的分类上进行拓展, 细分群体语言经验和个体语言经验的影响。

关键词: 语言经验, 概率线索, 词切分

Abstract:

Ample statistical learning (SL) studies have shown that individuals can perform word segmentation by tracking the likelihood of syllable co-occurrences in continuous speech. The classic “exposure-test” paradigm was widely used in this field, in which participants were first exposed to an artificial language and then tested in a forced choice task to assess learning effects. Recently, research has shown that participants' linguistic background, including their phonological and lexical experience, may result in experience-dependent SL. After a systematic review, we also discuss the direction for future SL studies. Specifically, we suggest that for studies involving Mandarin native speakers, researchers should carefully examine the separate and combined effects of various linguistic experience in order to better understand statistical word segmentation.

Key words: linguistic experience, statistical information, word segmentation

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