| 2017 — 2019 |
Pogue, Amanda
Kurumada, Chigusa [⬀] |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Doctoral Dissertation Research: the Interaction of Expectations and Evidence in Pragmatic Inference and Generalizations @ University of Rochester
Spoken language not only communicates information about a speaker's thoughts or desires; it also conveys information about the speaker's identity. By simply listening to speakers' voices, accents, and word choice, we can learn a great deal about them, in addition to what is being talked about. Previous studies of language processing, however, have almost exclusively focused on the linguistic signal abstracted from individual speakers, investigating what listeners think is true about the world based on what an individual speaker has said. The project aims to explore the mechanism by which listeners extract information about the speaker through processing the linguistic signal. It then addresses the question of whether, and if so how, the increased knowledge about the speaker facilitates language comprehension. This research, consequently allows researchers to build a foundation for exploring how young children may learn speaker differences, which can contribute to new pedagogical tools for helping children to better interact with, and learn from, diverse populations. Secondly, the work will likely have industry applications for artificial intelligence technology, allowing it to better adapt its functionality to an individual user's talking style.
This dissertation project employs two approaches to investigating what information listeners extract from spoken utterances. First, a large-scale online survey technique will be used to solicit responses from participants from a wider variety of linguistic and cultural backgrounds than those included in previous studies. Participants are exposed to utterances produced by two speakers and subsequently answer questions that probe their sensitivity to across-speaker differences. In the second set of experiments, a combination of an artificial language learning paradigm and an eye-tracking methodology will be used to study real-time language comprehension behaviors. Listeners' eye-gaze will be used to gain fine-grained information about the real-time development of their linguistic expectations. By combining these experimental approaches, the researchers elucidate how the human language comprehension system derives fine-grained expectations for future linguistic input and how the mechanism develops as a function of increased knowledge about linguistic communication.
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