Alex Clonan
PhD Student Biomedical Engineering
University of Connecticut
Friday, March 6, 2026 10:45am – 11:45am; PWEB 150
Abstract: Human hearing remarkably and consistently integrates sensory information from our surrounding environment to inform decision making and facilitate auditory perception. For instance, amidst a busy city, one could simultaneously attend to conversation, become startled by and localize the presence of a police siren, all while in the presence of background noise. This sensory information is nonlinearly integrated along the auditory pathway to inform perceptual judgments required to navigate, communicate and experience the world. For individuals with misophonia, sounds commonly experienced in daily life can evoke severe discomfort and distress. Yet the mechanisms underlying these complex perceptual tasks and the method of emotional integration are not well understood and current diagnostics are limited to survey responses. Here, we explore whether bottom-up statistical sound features processed in the auditory periphery and midbrain can explain aversion to sounds. The modelling framework leverages biologically informed neural-acoustic features to create idiosyncratic subspaces of objective aversive features for each individual that correlate to existing survey diagnostics. We extrapolate the model to sound mixtures to provide a proof of concept for a novel personalized detection algorithm for aversive sounds in real environments. Beyond this, we explore preliminary approaches to individualized sound attenuation based on statistical sound features. Altogether, our results suggest that acoustic features – spectrotemporal modulations, in particular – can practically be used to characterize the individualized patterns of aversion in participants with misophonia. Future perceptual studies using synthetic sounds and sound sets with more diverse acoustics will allow model predictions to be tested more broadly; however, sound-computable models may already have applications in precision diagnosis and management of misophonia.
Biography: Alex Clonan is a fourth-year graduate student in the UConn Biomedical Engineering Department, conducting research under the supervision of Dr. Monty Escabí (BME/ECE/Psych) and Dr. Ian Stevenson (Psych/BME). His research focuses on computational models of human hearing, interpretable machine learning models and practical intervention methods. Specifically, Alex’s work is focused on complex perception of natural sounds. Alex received his undergraduate degrees from UConn in Electrical Engineering and Molecular Cell Biology. While doing his bachelor’s degrees, Alex started working in Dr. Monty Escabí’s physiological acoustics lab in 2020 as an undergraduate. Alex is also member of the Institute for Brain and Cognitive Sciences (IBACS) where he was an outreach fellow, helping coordinate STEM education initiatives surrounding neuroscience. Further, Alex acted as a graduate mentor for the Engineering House Learning community for 4 years, mentoring students and developing curriculum for service-learning based outreach projects.
For additional information, please contact Dr. Visar Ajeti or Darcy Richard
