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Incremental Processing Effects in Nominal Compounds.

机译:标称化合物中的增量加工效果。

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摘要

Sentence processing shows the effects of a series of continual building, repairing, predicting, accessing, and remembering operations that may be the output of one underlying process or many. Within even smaller phrases such as nominal compounds, we see all of these operations having an effect. What is relatively unknown, though, is how these processes interact with each other in real time as the phrase is build up incrementally. This ERP study, through the use of an Icelandic triple noun compound paradigm that manipulates agreement features on the first and second constituents of the compound, investigates the nature of commitments to a structure and the processes that predict more structure or revise an interpreted structure. The findings are generally in line with models that have a parser make commitments to a structure as soon as possible, and the findings expand on that to say that syntactic mismatch is sufficient to trigger a structural prediction and that a revision of that prediction is identical to the revision of a structure built from incoming lexical items. This study further uses the paradigm to assess the predictions of Gibson's (1998) model of sentence processing that makes use of working memory costs. The study finds that, when incremental commitments are taken into account, we see the effects of syntactic agreement cues modulating working memory effects.
机译:句子处理显示了一系列连续构建,修复,预测,访问和记忆操作的效果,这些操作可能是一个或多个基础过程的输出。在甚至更小的短语(例如标称化合物)中,我们看到所有这些操作都起作用。但是,相对不为人所知的是,随着短语的逐步建立,这些过程之间如何实时交互。这项ERP研究通过使用冰岛语三重名词复合范例来操纵该复合成分的第一和第二成分上的一致特征,从而研究了对结构的承诺的性质以及预测更多结构或修改解释结构的过程。这些发现通常与使解析器尽快对结构作出承诺的模型保持一致,并且发现的扩展之处在于语法不匹配足以触发结构预测,并且该预测的修订与根据传入的词汇项目构建的结构的修订版。这项研究进一步使用范式来评估Gibson(1998)利用工作记忆成本进行的句子处理模型的预测。研究发现,当考虑到增量承诺时,我们会看到句法一致性提示对调节工作记忆效应的影响。

著录项

  • 作者

    Parrish, Alicia.;

  • 作者单位

    Michigan State University.;

  • 授予单位 Michigan State University.;
  • 学科 Linguistics.
  • 学位 M.A.
  • 年度 2017
  • 页码 72 p.
  • 总页数 72
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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