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Active STANDARD GRANT National Science Foundation (US)

Doctoral Dissertation Research: A descriptive grammar of an emerging sign language

$176.4K USD

Funder National Science Foundation (US)
Recipient Organization University of Texas At Austin
Country United States
Start Date Apr 15, 2025
End Date Mar 31, 2026
Duration 350 days
Number of Grantees 2
Roles Principal Investigator; Co-Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2450953
Grant Description

Theories about how humans create language depend on what language structures are observed. Less is known about sign languages compared to spoken languages. The sign language examined in this doctoral dissertation project is important because it is a young language.

Studying young languages helps scientists understand how languages develop and change over time. In some theories, the structure of new languages depends on ways in which human brains are hard-wired to learn language from birth. In other theories, the structure of new languages comes mainly from the ways people communicate and interact with each other.

This doctoral dissertation project investigates a young sign language that has never been studied in detail by linguists before. The results reveal a timeline of how a language develops that could provide evidence for or against these theories, thus advancing a more comprehensive scientific understanding of human language. Other benefits to society include educational opportunities and workforce development for students involved in the research.

This doctoral dissertation project yields a sign language grammar and an archive of language samples. Data are gathered by interviewing three groups of people: those who were born before the language began, those who were children during the early period of the language, and those who grew up after the language was established. Past research has found that people keep using language features they learned when they were young.

Studying the language of different generations is used to infer language change over time. This study collects information about linguistic structures by having participants do communication tasks such as describing what they see in a photograph, reversing a sentence from a positive to a negative, or filling in a gap in a sentence. These tasks elicit specific expressions, including verbs in different tenses or different types of clauses.

The researchers also gather data on language transmission at the level of individual language users. Data and language samples are made available in a public archive, providing an important resource for education and research.

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.

All Grantees

University of Texas At Austin

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