Event Segmentation During Naturalistic Speech Listening
Description: Hearing loss is common among adults aged 60 year or older and often results in speech- comprehension difficulties in the presence of background noise. Predicting these difficulties has been limited because existing approaches have primarily used artificial speech materials that have little resemblance to speech in everyday life. Novel approaches leveraging naturalistic speech are critical to better understand speech- comprehension challenges and the underlying mechanisms. Research in memory domains demonstrate that while naturalistic visual or audiovisual episodes continuously evolve over time, we perceive and encode information as discrete events. Segmenting information into meaningful events is crucial for comprehension and recall. However, previous research has mostly investigated the segmentation process using visual (reading) and audiovisual (movies) materials. Little is known about how younger and older adults segment naturalistic, continuous speech into meaningful events under different degrees of background noise. The proposed research aims to better understand how listeners identify and encode events under different listening demands and elucidate the underlying neural mechanisms. Aim 1 will establish how younger and older adults segment naturalistic, spoken speech into events and how segmentation affects speech comprehension. Aim 2 will characterize how background noise affects segmentation and recall ability. Aim 3 will investigate the neural networks that support event segmentation and how event related neural activity is altered with background noise and age. This project will ultimately enhance our our understanding of speech processing in younger and older adults in everyday environments and will potentially inform more effective treatment approaches to enhance the quality of life of those living with hearing loss.
Amount: CA$118,300
Funding type: fellowship
Funder scheme: Doctoral Research Award: Canada Graduate Scholarships
Start date: 2024-05-01
End date: 2027-04-30
Investigator: Ryan Panela
Institution: Rotman Research Institute (Toronto, Ontario)
Funded outputs count: 0
Funder award ID: 193310_1
OpenAlex ID: G2275330284