Institute for Logic, Language and Computation, University of Amsterdam.
Psychol Sci. 2011 Jun;22(6):829-34. doi: 10.1177/0956797611409589. Epub 2011 May 17.
Although it is generally accepted that hierarchical phrase structures are instrumental in describing human language, their role in cognitive processing is still debated. We investigated the role of hierarchical structure in sentence processing by implementing a range of probabilistic language models, some of which depended on hierarchical structure, and others of which relied on sequential structure only. All models estimated the occurrence probabilities of syntactic categories in sentences for which reading-time data were available. Relating the models' probability estimates to the data showed that the hierarchical-structure models did not account for variance in reading times over and above the amount of variance accounted for by all of the sequential-structure models. This suggests that a sentence's hierarchical structure, unlike many other sources of information, does not noticeably affect the generation of expectations about upcoming words.
虽然人们普遍认为层次结构在描述人类语言方面起着重要作用,但它们在认知处理中的作用仍存在争议。我们通过实现一系列概率语言模型来研究层次结构在句子处理中的作用,其中一些模型依赖于层次结构,而另一些模型仅依赖于序列结构。所有模型都估计了阅读时间数据可用的句子中句法类别的出现概率。将模型的概率估计与数据相关联表明,层次结构模型并不能解释阅读时间的变化,而不仅仅是序列结构模型所解释的变化量。这表明,与许多其他信息源不同,句子的层次结构不会明显影响对后续单词的期望的产生。