The contribution of maternal language input and statistical learning to brain and vocabulary development among children from low SES backgrounds

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）计划和既定计划，以刺激竞争力的研究（EPSCoR）的一部分。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。在特拉华州大学的Zhenghan Qi博士的赞助下，该博士后奖学金支持早期职业科学家调查上级统计学习能力是否可以作为保护因素，防止来自低社会经济（SES）背景的不利影响对语言发展的影响。来自社会经济地位低的家庭会对语言和大脑发育产生负面影响。随着儿童在学校的进步，社会经济地位低和高的儿童之间的“成绩差距”加剧。虽然这可能是由于早期的环境因素，如质量和数量的父母输入早期，有大量的异质性，在词汇的结果，从低社会经济地位家庭的孩子。这种异质性的词汇结果表明，语言的成功是不完全占的数量和质量的母亲输入。事实上，在婴儿中，儿童指导的输入和儿童处理输入的能力占了两岁时词汇技能的大部分差异。这种在环境中提取和处理输入的能力，也称为统计学习（SL），通常被认为是第一语言发展的核心支持机制。因此，目前的建议旨在澄清如何通过统计学习（SL）在神经水平上的母亲输入和处理这种输入，占低SES儿童的词汇变异。这项研究是由迫切需要确定保护因素的儿童从低社会经济地位的家庭，鉴于普遍存在的词汇差距。此外，没有研究到目前为止已经解决了大量的异质性低SES人口，这对于理解为什么一些孩子从低SES家庭表现比其他人更好的措施vocabulary.The拟议的研究利用多模态，跨学科的方法来研究的异质性词汇知识的儿童从社会经济不利的环境。本研究以60名来自低社会经济地位家庭的儿童为被试，考察了二语习得能力、母子对话的质量和数量对低社会经济地位样本中词汇变异的影响。在同一样本中，本研究将使用功能磁共振成像在统计学习过程中比较好的和差的学习者之间的神经参与模式，并确定这些模式是否预测词汇量。目前的研究启动了一项调查，在低SES样本中的环境因素，认知和大脑激活对词汇变异的贡献，以隔离哪些因素对这个社会经济风险人群的语言成功最有影响力。该奖项反映了NSF的法定使命，并被认为是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的支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program and the Established Program to Stimulate Competitive Research (EPSCoR). 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. Zhenghan Qi at the University of Delaware, this postdoctoral fellowship award supports an early career scientist investigating whether superior statistical learning abilities serve as a protective factor against the detrimental effects coming from a low socioeconomic (SES) background can have upon language development. Coming from a low SES home negatively affects language and brain development. As children progress through school, this "achievement gap" between low and higher SES children is exacerbated. While this may be attributed to early environmental factors, such as poor quality and quantity of parental inputs early on, there is substantial heterogeneity in the vocabulary outcomes of children from low SES families. This heterogeneity in vocabulary outcomes suggests language success is not fully accounted for by quantity and quality of maternal input. In fact, in infants, both the child-directed input and the child's ability to process that input account for the majority of variance seen in vocabulary skills at two years of age. This ability to extract and process incoming input in the environment, also known as statistical learning (SL), is often considered a core supporting mechanism of first language development. The current proposal therefore seeks to clarify how both maternal input and the processing of this input via statistical learning (SL) at the neurological level, account for variability in vocabulary among low SES children. This research is driven by the critical need to identify protective factors for children from low SES families, given the pervasiveness of the vocabulary gap. Additionally, no research to date has addressed the substantial heterogeneity within a low SES population, which holds important insights for understanding why some children from low SES homes perform better than others on measures of vocabulary.The proposed research utilizes a multimodal, cross-disciplinary approach to studying the heterogeneity of vocabulary knowledge among children from socioeconomically adverse environments. This research will recruit sixty children from low SES households to examine the contribution that SL ability and quality and quantity of mother-child conversations have upon vocabulary variability within a low SES sample. Within this same sample, the current study will compare patterns of neural engagement using fMRI during statistical learning between good and poor learners and identify whether these patterns predict vocabulary size. The current study launches an investigation into the contribution of environmental factors, cognition, and brain activation on vocabulary variability within a low SES sample to isolate which factors are most influential for language success in this socioeconomically at-risk population.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.