Using quantitative methods to understand the impact of input on development

Nguyen, Dung Hoang, Storrs CT

Investigators

Abstract

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.