Postdoctoral Fellowship: SPRF: Developing Communicative Competence in Peer Interactions

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) and SBE's Linguistics 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 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. Robert Hawkins at Stanford University, this postdoctoral fellowship award supports an early career scientist investigating child language acquisition in peer interactions. As children learn language, they are learning more than words and grammatical forms; they are learning to use language in conversation.

Crucially, conversational fluency requires not only being able to communicate with their earliest language teachers—their caregivers—but with a dizzying array of other interlocutors. The bulk of research addressing discourse skill development has focused on parent-child interactions in the home, limiting our understanding of how children learn to adapt to different interlocutors and generalize across them.

The proposed research will collect and analyze a large dataset of child-child conversations to examine how children learn to make different conversational moves, manage misunderstandings, and get things done in conversation with their peers. The project will provide a large dataset and suite of analytical tools that allow the research community to better understand the flexibility of children’s language skills across ages and contexts.

These data lay the groundwork for characterizing children’s language learning across disparate contexts, particularly those in which peer interactions are a central setting for language learning and socialization.

In this proposal, the fellow will advance the infrastructure for studying discourse development across different social contexts. The proposed project will fill a longstanding gap in the empirical literature by collecting and sharing a large corpus of naturalistic child-child conversation at preschool. The fellow will also collect observations of parent-child conversation for a subset of these children to provide a direct comparison between child-child discourse and better-studied caregiver contexts.

Using existing conversational corpora and this new corpus, the fellow will apply Natural Language Processing methods to develop a computational model comparing communicative act use in adult-adult, parent-child, and child-child conversations. Finally, the fellow will collect child-child and parent-child conversations in a controlled, collaborative task to test specific hypotheses about how interlocutors manage the communicative workload, including that the breadth of one's communicative act repertoire undergirds communicative success.

By leveraging newly available tools in data collection and computational modeling, this work will place the study of children’s everyday conversation on more rigorous empirical footing, shedding light on this little-understood sandbox for language development

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.