Doctoral Dissertation Research:Do individual differences in statistical learning predict linguistic adaptation?

博士论文研究：统计学习的个体差异能否预测语言适应？

• 批准号: 1451652
• 负责人: Steven Weinberger
• 金额: $ 1.28万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451652&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Do; individual

A rich body of research has shown that statistical properties of language, like word or sound frequencies and co-occurrences, provide crucial information used by children to learn their native language. At the same time, there is evidence that adults' language use is influenced by expectations - e.g., about likely upcoming sounds, words, structures - which are dynamic and can be rapidly updated based on the current linguistic environment. This project investigates the idea that these abilities in children and adults reflect the same underlying mechanism: implicit tracking of statistical information. This single mechanism would then be used both during language learning, and continually across the lifespan to adapt to new situations or speakers. This research aims to provide evidence for this connection by showing that an individual's ability to track statistical information when learning a new language is correlated with their ability to adapt their own language under changing conditions.This project will focus first on learning and adaptation of higher-level linguistic structures. The study will test whether an individual's performance on a classic statistical learning task - implicitly learning that the presence of one syllable depends on the presence of another - predicts their ability to rapidly adapt expectations regarding upcoming syntactic structures (e.g., whether a verb is likely to act as a main verb or introduce a relative clause). The study will then look for evidence of the generality of the mechanism in question by testing whether statistical learning of higher-level structures predicts adaptation of lower-level sound category information. The results of the project will have implications for second language research and teaching; if individual differences in statistical learning and adaptation reflect a mechanism active across the lifespan, the kinds of tasks used here could be developed as diagnostic tools for assessment of language ability, and how it changes with age. This project also involves developing new open-source web-based tools for conducting cognitive psychology and psycholinguistics research more quickly and with fewer resources.

大量研究表明，语言的统计特性，如单词或声音的频率和共现，为儿童学习母语提供了重要信息。与此同时，有证据表明，成年人的语言使用受到期望的影响-例如，关于可能即将出现的声音，单词，结构-这是动态的，可以根据当前的语言环境快速更新。这个项目调查的想法，这些能力在儿童和成人反映了相同的潜在机制：隐式跟踪的统计信息。这种单一的机制将在语言学习过程中使用，并在整个生命周期中不断适应新的情况或说话者。本研究的目的是通过证明一个人在学习一门新语言时跟踪统计信息的能力与他们在不断变化的条件下适应自己语言的能力之间的相关性来为这种联系提供证据。本项目将首先关注高级语言结构的学习和适应。这项研究将测试一个人在经典的统计学习任务上的表现-隐含地学习一个音节的存在取决于另一个音节的存在-预测他们快速适应即将到来的句法结构的期望的能力（例如，动词是否可能充当主要动词或引入关系从句）。然后，该研究将寻找证据的一般性机制的问题，通过测试是否较高层次的结构的统计学习预测适应较低层次的声音类别信息。该项目的结果将对第二语言研究和教学产生影响;如果统计学习和适应的个体差异反映了一种在整个生命周期中活跃的机制，那么这里使用的各种任务可以作为评估语言能力的诊断工具，以及它如何随着年龄的变化而变化。该项目还涉及开发新的基于网络的开源工具，以便更快地以更少的资源进行认知心理学和心理语言学研究。