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

该奖项是美国国家科学基金会社会、行为和经济科学博士后研究奖学金（SPRF）计划和促进竞争性研究的既定计划（EPSCoR）的一部分。SPRF计划的目标是为学术界、工业界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奖励包括在知名科学家的赞助下进行为期两年的培训，并鼓励博士后进行独立研究。美国国家科学基金会寻求促进科学界各阶层的科学家，包括那些未被充分代表的群体的科学家，参与其研究项目和活动；博士后阶段被认为是实现这一目标的一个重要的专业发展阶段。每个博士后必须解决各自学科领域的重要科学问题。在特拉华大学（University of Delaware）齐正汉博士（Dr. Zhenghan Qi）的赞助下，该博士后奖学金支持一位早期职业科学家，研究卓越的统计学习能力是否可以作为一种保护因素，抵御来自低社会经济背景（SES）对语言发展的不利影响。来自低社会经济地位家庭对语言和大脑发育有负面影响。随着孩子们在学校的进步，社会经济地位低的孩子和社会经济地位高的孩子之间的“成就差距”会加剧。虽然这可能归因于早期的环境因素，如早期父母输入的质量和数量较差，但来自低社会经济地位家庭的儿童的词汇结果存在很大的异质性。这种词汇结果的异质性表明，语言的成功并不能完全由母亲输入的数量和质量来解释。事实上，在婴儿中，孩子直接输入和孩子处理输入的能力是两岁时词汇技能差异的主要原因。这种提取和处理环境输入的能力，也被称为统计学习（SL），通常被认为是第一语言发展的核心支持机制。因此，目前的建议旨在阐明母亲的输入和通过神经学水平的统计学习（SL）对这种输入的处理如何解释低社会经济地位儿童的词汇差异。考虑到普遍存在的词汇差距，这项研究是由于迫切需要确定来自低社会经济地位家庭的儿童的保护因素。此外，到目前为止，还没有研究解决低社会经济地位人群中的实质性异质性，这对于理解为什么一些来自低社会经济地位家庭的孩子在词汇测量上比其他人表现得更好具有重要的见解。本研究采用多模态、跨学科的方法来研究来自社会经济不利环境的儿童词汇知识的异质性。本研究将招募60名来自低社会经济地位家庭的儿童，在低社会经济地位的样本中检验母语能力、母子对话的质量和数量对词汇变异的贡献。在相同的样本中，本研究将使用功能磁共振成像比较优秀和较差学习者在统计学习期间的神经参与模式，并确定这些模式是否预测词汇量。本研究对低社会经济地位人群中环境因素、认知和大脑活动对词汇变化的影响进行了调查，以找出哪些因素对这一社会经济风险人群的语言成功影响最大。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.