All events

Confratute 2025 Overview: Over 25 strands that are intensive, three-day mini-courses. Keynotes on relevant research and trends in regular and gifted education. Special Topic sessions on Al, creativity, thinking skills, underachievement, and more. SEM leaders Forum strands on curriculum development and other leadership topics, designed for principals and other administrators. Learn more and register at Confratute (https://confratute.uconn.edu).

Art exhibit highlighting creative the creative talent of UConn Health Employees from across the organization.

"Expressions in Multimedia" by Martha G. Trask "Secondary Effects" by Jeff Ostergren Join us for a reception Thursday, May 22, from 4 to 6 p.m. in the Celeste LeWitt Gallery. (north side of the food court)Martha G. Trask is an expressive mixed media artist who happens to work in our library.Jeff Ostergren infuses his paint with actual medications to tell stories about the intertwined histories of pharmaceuticals and color.

The exhibiting artists are Marsha Borden, Helena Chastel, Kathryn Frund, Phoebe Godfrey, Hugh MacDonald, Bob Pavlik, Dan Potter, and R. Douglass Rice. Open Air 2025 is open daily and will remain on view through October 6, 2025. June 19, 2025 iis the last day to visit indoor art exhibitions. Exhibitions inside the AVS Gallery will resume on September 11, 2025

UConn Health Day 2 new employee orientation is conducted on Saba. It focuses on comprehensive training for our newest workforce, covering diversity awareness, sexual harassment prevention, and compliance to ensure a respectful and compliant work environment.AgendaDay 2Format: Saba Self-Guided Learning Time: 8 am - 4:30 pm Location: RemoteActivities: Dive deeper into your compliance and role-specific training and explore resources at your own pace.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

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.

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.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).