Using quantitative methods to understand the impact of input on development

该奖项是作为NSF的社会，行为和经济科学博士后研究奖学金（SPRF）计划的一部分提供的。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在加州大学欧文分校的丽莎珍珠博士的赞助下，这个博士后奖学金支持一个早期的职业科学家调查儿童复杂的语言知识的发展，同时考虑到由于文化因素输入变化的影响。特别是，这个项目调查了什么构成“发展有意义”的输入-也就是说，输入质量上影响语言发展。具体来说，它着眼于英语学习的儿童如何从他们的输入中提取词汇信息，以理解被动语态的句子，以及这个过程是否在学习相同语言的社会经济地位（SES）人群中是不同的。拟议项目的目标是提供一个精确的和包容性的理论，如何典型的发展中国家的儿童学习被动语态，以及如何在他们的语言环境的变化可能会产生积极或消极的影响学习。达到一个典型的发展儿童的行为结果的基础上，他们的语言环境的充分理解将提供关键的洞察语言偏差的来源，我们可能会观察到的儿童从临床人群。因此，拟议项目的结果将有助于通过针对儿童语言输入质量的干预措施来缩小儿童语言差距。这一目标将通过一个定量框架来实现，该框架包括理论，计算，语料库和行为方法。该项目将首先建立一个精确的理论，说明儿童如何通过计算建模在他们的输入中利用词汇信息，预测语言输入如何导致语言知识的发展。这些计算模型将适用于相关词汇特征的理论和通过在线众包平台进行的行为研究得出的目标成人知识的基线。为了研究什么构成了学习被动句的发展有意义的输入，将建立和分析SES人群中儿童导向语音的词汇信息语料库。通过我们的计算模型预测的行为差异将使我们能够评估基于输入的干预措施的潜在有效性，以减轻在被动语态发展中发现的（任何）基于输入的差异。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Lisa Pearl at the University of California, Irvine, this postdoctoral fellowship award supports an early career scientist investigating children’s development of complex linguistic knowledge, while considering the impact of input variation due to cultural factors. In particular, this project investigates what constitutes “developmentally meaningful” input – that is, input that qualitatively impacts language development. Specifically, it looks at how English-learning children extract lexical information from their input in order to comprehend sentences in the passive voice, and whether this process is different across socio-economic status (SES) populations learning the same language. The goal of the proposed project is to provide a precise and inclusive theory of how typically-developing children learn the passive voice and how changes in their linguistic environment may positively or negatively impact learning. Reaching a full understanding of the behavioral outcomes of typically-developing children based on their linguistic environment will provide crucial insight into the source of linguistic deviations that we may observe in children from clinical populations. Results from the proposed project will thereby help inform efforts at closing language gaps in children through interventions targeting the quality of children’s language input.This goal will be accomplished through a quantitative framework that incorporates theoretical, computational, corpus, and behavioral approaches. The project will first build a precise theory of how children can harness lexical information in their input via computational modeling, predicting how linguistic input causes linguistic knowledge to develop. These computational models will be fitted for a theory of relevant lexical features and a baseline of the target adult knowledge derived from behavioral studies conducted through online crowd-sourcing platforms. To investigate what constitutes developmentally-meaningful input for learning passives, a corpus of lexical information for child-directed speech across SES populations will be built and analyzed. Predicted behavioral differences by our computational models will allow us to assess the potential efficacy of input-based interventions for mitigating (any) input-based differences found in the development of the passive voice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.