Angie Li
Pronouns: She/Her
Research Mentor(s): Anahita Mehta
Research Mentor School/College/Department: Otolaryngology-Head and Neck Surgery / Medicine
Program:
Authors: Angie Li, Anthony Chen, Jackson Graves, Anahita Mehta Mehta
Session: Session 1: 9:00 am – 9:50 am
Poster: 28
Abstract
Pitch is a crucial acoustic feature used not only in music perception, but also to help discern sounds in noisy environments, and localize sounds. My research project aims to understand how the pitch of complex sounds is processed in the human auditory system. Our project uses carefully constructed stimuli based on computational models of the auditory system, and 4 behavioral pitch tasks to elucidate the role of the auditory nerve firing rate in pitch perception in normal hearing participants. We aim to have 20 participants in our study; each participant completing 5 sessions of 2 hours each. My role includes participant recruitment, data collection, visualization, and analysis of the data. The lab experience also provides an opportunity to learn experimental design and gain skills in MATLAB. Data collection will be completed by the end of the summer. However, analyses and writing up the experiment along with follow-up experiments will continue in the fall. The experiment involves the use of “metamers”, stimuli with identical outputs according to one model of pitch perception, the rate-place model. The behavioral findings show that in most circumstances, these model metamers preserve the cues required for pitch perception, which implies that rate-place information is primarily responsible for pitch perception, even for spectrally rich stimuli such as combinations of three F0s, where the rate-place code is degraded. The significance of the research lies in investigating the essential cues required to process complex pitch. Currently, there is no consensus in the field on how pitch is encoded, resulting in a lack of understanding of how challenges in pitch perception due to hearing loss can be helped. Understanding these pitch coding mechanisms will help inform sound processing algorithms for auditory prostheses, such as cochlear implants and hearing aids.
