Morphological Decomposition in Derived Word Recognition: Single Trial Correlational MEG Studies of Morphology Down to the Roots

派生词识别中的形态分解：从根源上进行形态学的单次试验相关 MEG 研究

• 批准号: 0843969
• 负责人: Alec Marantz
• 金额: $ 61.29万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0843969&HistoricalAwards=false
• 关键词: Morphological; Decomposition; Derived; Word; Recognition

This project will investigate the comprehension of morphologically complex words, words like "knowable" that can be broken down into pieces. Linguists and psycholinguists disagree on whether all words that consist of a possibly independent stem ("know" is a verb by itself) and an identifiable suffix ("-able" generally creates adjectives from verbs) are analyzed as complex by speakers and whether all or any such words are recognized via decomposition into their parts. The issue is particularly controversial for words like "tolerable" apparently built from roots that do not appear on their own ("toler-" is also seen in "tolerate" but not elsewhere). This project uses magnetoencephalographic (MEG) brain monitoring methods to test a theory about the interaction of linguistic representations and neural computations. The theory demands that every decomposition motivated by linguistic theory is a necessary computational step in recognizing a word and that each computation maps to neural activity of a set of brain regions at particular time latencies during word recognition. For this project, subjects will read or listen to individual words and non-words, judging their word status, while the electrical activity in their brains is monitored with MEG. A novel analysis technique will be developed that correlates the brain responses of each subject for each word with continuous stimulus variables such as word frequency, suffix frequency, and the probability of having a particular suffix following a particular stem. Given sufficient numbers of subjects and stimuli, this technique can provide meaningful data about individual words and individual subjects. For example, is a word like "vulnerable", whose root "vulner-" does not appear elsewhere in English, recognized in the same way as "knowable" or "tolerable"? The project should elucidate the connections among linguistic theory, psycholinguistic models, and brain activity while testing hypotheses about the comprehension of morphologically complex words. Since for the theories of morphology being tested, the structure of words involves the same computations and representations as the structure of sentences, support for full decomposition to the root for words like "knowable" and "tolerable" will have implications for language processing at all levels of linguistic analysis. The single trial MEG analysis techniques developed by the project should aid greatly in the diagnosis of language-impaired populations and in the evaluation of remediation. Exploiting the results and techniques of this project, future research can ask, for example, how the brain responses of an individual dyslexic or autistic subject differ from the norm along various dimensions, and if any of these responses approach group norms after intervention. Progress in understanding and treating these deficits should come from an understanding of how neural systems perform linguistic computations and store and manipulate linguistic representations.

这个项目将研究对词形复杂的词的理解，比如“可知的”，这些词可以被分解成碎片。对于是否所有由可能独立词干（“know”本身就是一个动词）和一个可识别后缀（“-able”通常由动词构成形容词）组成的单词都被说话者分析为复数，以及是否所有或任何这样的单词都是通过分解成它们的部分来识别的，语言学家和心理语言学家存在分歧。这个问题尤其有争议，因为像“tolerable”这样的词显然是由不单独出现的词根组成的（“toler-”也出现在“tolerate”中，但在其他地方没有）。这个项目使用脑磁图（MEG）大脑监测方法来测试一个关于语言表征和神经计算相互作用的理论。该理论要求每一个由语言理论驱动的分解都是识别单词的必要计算步骤，并且每个计算都映射到单词识别过程中特定时间潜伏期的一组大脑区域的神经活动。在这个项目中，受试者将阅读或听单个单词和非单词，判断他们的单词状态，同时用MEG监测他们的脑电活动。将开发一种新的分析技术，将每个受试者对每个单词的大脑反应与连续的刺激变量（如词频、词尾频率和特定词干后出现特定后缀的概率）联系起来。在给定足够数量的受试者和刺激的情况下，该技术可以提供关于单个单词和单个受试者的有意义的数据。例如，像“vulnerable”这样的单词，其词根“vulner-”在英语的其他地方没有出现，是否可以像“可知的”或“可容忍的”那样被识别？该项目应阐明语言学理论、心理语言学模型和大脑活动之间的联系，同时测试关于理解词形复杂单词的假设。因为对于被测试的形态学理论来说，单词的结构涉及与句子结构相同的计算和表示，所以支持“可知”和“可容忍”等单词的完全分解到词根将对语言分析的各个层面的语言处理产生影响。该项目开发的单次试验脑磁图分析技术将大大有助于语言障碍人群的诊断和评估补救措施。利用这个项目的结果和技术，未来的研究可以问，例如，一个阅读障碍或自闭症个体的大脑反应在各个方面与正常情况有何不同，以及干预后这些反应中是否有任何反应接近群体标准。理解和治疗这些缺陷的进展应该来自对神经系统如何进行语言计算以及如何存储和操纵语言表征的理解。