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Doctoral Dissertation Research: The role of social information in sentence processing: Adjectives, nouns, and contextual cues
NSF
About This Grant
Social information can be linguistically represented in many ways, such as with word endings or as part of the word’s definition. For example, some words are stereotypically gendered because the word itself is not linguistically marked for gender, but it still carries information influenced by social experiences. This doctoral dissertation project advances an understanding of how the various representations of social information in nouns differently impact language comprehension and processing. Additionally, this project tests possible social information in adjectives, which has not been previously studied in language processing. Additional benefits to society include educational and workforce development opportunities for undergraduate students who receive training in psycholinguistic research methods. Research in linguistics shows that gender bias plays a crucial role when forming a coreferential dependency, which is the process of linking a pronoun with a larger noun phrase that the pronoun replaces and refers to. Influence of gender on coreferential dependency formation has been found in online processing measures, with a slowdown in reading times when a noun phrase, though grammatically accessible, is judged by the perceiver as “mismatching” the pronoun in gender. This project uses eye-tracking measures to investigate how differences in processing may reflect differences in the social representation of a word and/or differences in the sentence structure and context surrounding the target word. Additionally, new methods for testing social information are developed. De-identified data and code are made publicly available, as a major goal of this project is providing updated norming results on nouns, as well the first publicly available norming results on adjectives. 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.
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How to Apply
Up to $16K
2027-01-31
One-time $99 fee · Includes AI drafting + templates + PDF export
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