Josie Welkom Miranda
$138,000
Edward A Gibson
Evelina Fedorenko
Floyd, Samantha B
Connecticut
Social Behavioral and Economic Sciences (SBE)
SPRF-Broadening Participation
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. Edward Gibson and Dr. Evelina Fedorenko, this postdoctoral fellowship award supports an early career scientist examining how non-literal language is understood by humans and machines. Much of human communication is not encoded directly in words: we may say ‘It’s getting late’ to politely indicate that we would like to leave, or call a ballerina a ‘swan’ to capture her grace. This kind of language is called pragmatics and often thought to comprise everything from humor, to white lies, metaphors, implicature, prosody, and more. Although there have been investigations into each of these phenomena individually, it is unknown whether they are supported by the same mechanisms, nor how they relate within the individual, which has implications for neurodivergent individuals facing challenges in communication. And, while current language models show impressive results, little research has explored what kinds of computations might support pragmatic language understanding, which is crucial for success in artificial intelligence. Across both behavioral and computational approaches, the current project will address these limitations by identifying clusters of related pragmatic inferences in humans and exploring how they are computed in models. <br/><br/>This project applies new methods to shed light on a unified framework for pragmatic language, with the goal of including neurodivergent individuals as researchers in the process. Large-scale individual-differences studies will develop a comprehensive battery of pragmatic language and expose relationships between abilities (e.g., does understanding white lies correlate best with understanding of irony?). Non-linguistic cognitive assessments will identify which clusters relate to social reasoning, literal language understanding, and executive abilities. By evaluating current language models on non-literal language interpretation, the project with also uncover the learnability, scope, and generalization of language models’ performance on pragmatic language, and will allow us to compare model activity to the clusters found in the human studies. By testing pragmatic language in humans and current computational models, this research will bring us closer to understanding the necessary input and computations for pragmatic language comprehension.<br/><br/>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.