Inherent envelope fluctuations in forward masking: Effects of age and hearing loss

Abstract

Forward masking is generally greater for Gaussian noise (GN) than for low-fluctuation noise maskers, i.e., GN disruption. Because the minimal hearing loss that is associated with older age may affect GN disruption differently than more significant hearing loss, the current study explored the contribution of minimal hearing loss associated with older age to GN disruption. GN disruption was measured using three masker-signal delays (25, 75, and 150 ms) for three adult groups: younger participants with normal hearing (NH), older participants with minimal hearing loss, and older participants with sensorineural hearing loss. The role of underlying mechanisms was tested using a computational model for midbrain neurons. The primary result suggests that older listeners with mild threshold elevations that typically occur with age may be more susceptible to the deleterious effects of masker envelope fluctuations than younger listeners with NH. Results from the computational model indicate that there may be a larger influence of efferent feedback and saturation of inner hair cells on forward masking and GN disruption than previously considered.

Auditory Forward Masking Explained by a Subcortical Model with Efferent Control of Cochlear Gain

“…For the physiological simulations, on each trial, the interval with the higher maximum response during a 40-ms time window was selected as the target interval. The use of maximum response during a time window to detect onset responses was based on the modeling approach of Brennan et al (2023). The time window used for the physiological simulations matched the analysis windows used in Nelson et al (2009) and Ingham et al (2016), and is shown as a short solid horizontal line in Fig.…”

“…The modeling approach was similar to that used for predictions of forward-masked thresholds in Brennan et al (2023). Two models of the AN were used to simulate neural responses with (Farhadi et al, 2023) and without (Zilany et al, 2014) gain control by MOC efferents.…”

A fast and flexible approximation of power-law adaptation for auditory computational models

“…These models have proven invaluable for interrelating data across different species, modalities, and paradigms. For example, the phenomenological auditory-nerve (AN) model of Zilany, Bruce, and Carney (2014), hereafter the ZBC model, has been used extensively by several groups in recent years to pursue a wide range of psychophysical and physiological questions (Carney and McDonough, 2019; Bianchi et al, 2019; Maxwell et al, 2020; Saddler et al, 2021; Polonenko and Maddox, 2021; Zaar and Carney, 2022; Guest and Oxenham, 2022; Feather et al, 2022; Carney et al, 2023; Hamza et al, 2023; Lindboom et al, 2023; Brennan et al, 2023). Differences among models often reflect differences in the underlying goals of those who build them (Osses et al, 2022).…”