MTS is the department’s Mind, Technology, and Society speaker series. It is hosted by a different faculty member each semester. Founded by a generous gift from Professors Robert Glushko and Pamela Samuelson, MTS brings researchers and industry professionals from across the globe to present a variety of interdisciplinary work in cognitive science. See our UCMerced CogSci youtube channel for videos of past MTS talks!
CIS graduate students, faculty, and staff, and all who are interested are invited! Members of other departments at UC Merced as well as the general public are encouraged to attend. (Note: current CIS Ph.D. students are required to attend MTS each semester in residence, to fulfill their COGS 250 course requirement).
Dr. Ryskin's talk "Noisy-Channel Inference: A Unifying Framework for Language Comprehension and Processing" will be 3-4:30pm in COB 116.
Abstract: Human language comprehension is remarkably robust: across myriad contexts we can grasp what speakers intend and even understand them when they misspeak. But it is also susceptible to breakdown. We sometimes fail to reach a shared understanding or arrive at interpretations incompatible with the literal input. In this talk, I will argue that the most promising theory of both robustness and breakdown in language processing is the "Noisy Channel" theory. This account views comprehension as probabilistic inference under uncertainty — recovering intended meaning from imperfect input by combining "noisy" evidence with prior knowledge. I will provide evidence in support of multiple predictions of this account and discuss new directions of inquiry opened up by viewing human language processing through a noisy-channel lens.
Bio: I’m an Associate Professor of Cognitive & Information Sciences at the University of California, Merced. Check out the Language, Interaction, & Cognition (LInC) lab webpage for more details! I study how individuals make sophisticated inferences, in real time, during communication in order to extract meaning from language input that can be noisy and ambiguous. I combine insights from eye-tracking, EEG, computational approaches, fieldwork, and neuropsychology to understand 1) how people use various sources of information (visuo-spatial perspective, the speaker’s knowledge state, language statistics, etc.) to generate and constrain their linguistic predictions, and 2) how lifelong learning allows inferences to be adaptive when the environment changes.
For more information or to sign up for email announcements, please contact the talk series organizer: cis-mts-lead@lists.ucmerced.edu.
