Event

ANNOUNCEMENT: M.S. Thesis Defense

 

Name: Morgan Belcher

 

Committee:

 

Date/Time: Wednesday, August 7th, 2024 at 12pm 

 

Location: AVW 1146

 

 

Abstract:

This thesis investigates the spectrotemporal characteristics of neural signals acquired through magnetoencephalography (MEG) while subjects engage in a tone cloud listening task. The tone cloud, or stochastic figure-ground (SFG) task requires that participants segregate inharmonic chords from a background of randomly varying pure tones. This task has key advantages over many current methodologies that predominantly utilize linguistic-based paradigms to examine auditory stream segregation, which may limit their applicability across diverse populations, including those with language impairments, non-native speakers, and older listeners. By employing a non-linguistic-based auditory measure, this study aims to explore the potential for a universal auditory processing assessment tool. Through detailed spectrotemporal and temporal analysis, this research explores the neural dynamics underlying auditory stream segregation, offering insights into alternative approaches for evaluation of auditory stream segregation in listeners. The findings suggest that non-linguistic-based measures like tone cloud can potentially serve as effective replacements for traditional linguistic-based paradigms as a measure of how well listeners can perceptually segregate sounds in a noisy environment.