Shawn Cummings, in the Language & Cognition division of the Department of Psychological Sciences, will be defending his dissertation: "Linking Lexically Guided Perceptual Learning to Statistical Patterns in Speech Input". ABSTRACT: Listeners use lexical information to modify the mapping between speech acoustics and speech sound categories. Despite convention to consider lexically guided perceptual learning as a binary outcome, the magnitude of the learning effect varies in the extant literature. We hypothesize that graded learning outcomes can be linked, in part, to statistical characteristics of the to-be-learned input, consistent with the ideal adapter theory of speech adaptation. We begin with creation and analysis of a lexically guided perceptual learning corpus including stimulus sets for the /ʃ/-/s/ contrast for each of 16 talkers following standard methods (i.e., waveform averaging to create ambiguous variants), yielding variability in the statistical cues specifying this contrast across talkers (Experiment 2). We then analyze the perceptual consequences of this variability on perception prior to experimentally induced bias (Experiment 2). Finally, we (a) measure lexically guided perceptual learning for each talker, (b) identify input characteristics that are associated with learning magnitude, and (c) examine whether a computational instantiation of the ideal adapter theory can model the input-learning link (Experiment 3). Robust learning is observed for 13 of 16 talkers, with magnitudes of learning strongly convergent between behavior and model simulations. These results provide a critical and successful test of the ideal adapter framework for speech adaptation, thus informing an understanding of the mechanisms that allow listeners to solve the lack of invariance problem for speech perception.

U n i v e r s i t y of C o n n e c t i c u t DEPARTMENT OF PATHOBIOLOGY & VETERINARY SCIENCE Doctoral Dissertation Defense of Elizabeth Ramirez-Medina Major Advisors: Dr. Guillermo Risatti Associate Advisors: Dr. Paulo Verardi, Dr. Antonio Garmendia and Dr. Manuel Borca Title: "Assessment of the biological function of African Swine Fever Virus genes" Wednesday, July 16, 2025, 11am ESB 121 To attend virtually via WebEx please use the following link: https://uconn-cmr.webex.com/uconncmr/j.php?MTID=me1071857f38dad6d504c2b27e7f0b96d

Who: Sachin Tripathi, PhD Candidate in Civil Engineering (Structural Engineering). What: PhD Dissertation Defense for the dissertation titled: "Three-Dimensional Temperature Distribution and Thermal Induced Responses of a Lunar Habitat Structure Considering Regolith Cover and Self Shadow Effect." Why: As lunar exploration advances, this study addresses critical challenges in designing safe, sustainable lunar habitats. The findings provide vital insights for robust structural designs, contributing significantly to structural engineering for extraterrestrial environments. This defense is an opportunity for the UConn community, including students, faculty, and researchers, to engage with cutting-edge research for future lunar missions.

Covariate-Assisted Stochastic Block Models: Feature Selection and Latent Factors

Dissertation title: Transforming Elegies in Romantic Britain and Republican China Date: Monday, July 17 Time: 10 am to 12pm Place: SHH 236 Committee Charles Mahoney (Co-major advisor) Liansu Meng (Co-major advisor) Peter Zarrow (associate advisor) Peter Constantine (reader) Johannes Turk (external reader) Victor Zatsepine (reader) If you are not able to attend the Dissertation Defense in person, please see Webex meeting link below. Webex Meeting link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=me16a76c50c43317ed83f93b7a097f4fe Meeting number: 2868 631 4780 Meeting password: nJPW2ncmX72 Join from a video system or application Dial 28686314780@uconn-cmr.webex.com You can also dial 173.243.2.68 and enter your meeting number. Join by phone +1-415-655-0002 Toll Access code: 28686314780 Global call-in numbers https://uconn-cmr.webex.com/uconn-cmr/globalcallin.php?MTID=m03db380aee7590cee3bbc96c6f4d99c6