解码不可思议的:通过杂的通道模型解释普通话句子
Ruihua Mao1, Sihan Chen2, Edward Gibson2
1Laboratoire de Linguistique Formelle, Université Paris Cité, Bât. Olympe de Gouges, 5ème étage, 8, Rue Albert Einstein, 75013, Paris, France. ruihua.mao@etu.u-paris.fr.
Memory & cognition
|October 3, 2025
概括
这项研究测试了普通话中文的杂通道语言理解模型. 中国人对单个形态错误的句子做出了最多的推断,支持噪音道假设.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 噪音频道语言理解建议建议读者识别和修复句子解释错误.
- 之前的研究 (Zhan等人,2023) 在英语中探索了这一点.
研究的目的:
- 为了测试噪音道模型对普通话中文的适用性.
- 研究不同语法结构如何影响语言理解过程中的错误纠正.
主要方法:
- 复制和扩展的 Zhan 等. (2023) 使用三种普通话中文语法交替.
- 参与者阅读不可思议的句子,并回答理解问题,以衡量字面解释.
主要成果:
- 在单个形态词删除/插入的句子中,普通话说者的推断率最高.
- 在函数词之间进行名词短语交换的推理率下降,然后在主要动词之间进行.
- 结果与Zhan等人一致. (2023年),证实了噪音频道框架的稳定性.
结论:
- 噪音频道框架有效地解释了普通话中文的语言理解.
- 语言特定的属性显著影响理解者如何处理和纠正句子错误.
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