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

Schneider, Julie Marie, Newark DE

Investigators

Abstract

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.