Computational Modeling of Individual Differences in Behavioral Estimates of Cochlear Nonlinearities

Abstract

Temporal masking curves (TMCs) are often used to estimate cochlear compression in individuals with normal and impaired hearing. These estimates may yield a wide range of individual differences, even among subjects with similar quiet thresholds. This study used an auditory model to assess potential sources of variance in TMCs from 51 listeners in Poling et al. [J Assoc Res Otolaryngol, 13:91-108 (2012)]. These sources included threshold elevation, the contribution of outer and inner hair cell dysfunction to threshold elevation, compression of the off-frequency linear reference, and detection efficiency. Simulations suggest that detection efficiency is a primary factor contributing to individual differences in TMCs measured in normal-hearing subjects, while threshold elevation and the contribution of outer and inner hair cell dysfunction are primary factors in hearing-impaired subjects. Approximating the most compressive growth rate of the cochlear response from TMCs was achieved only in subjects with the highest detection efficiency. Simulations included off-frequency nonlinearity in basilar membrane and inner hair cell processing; however, this nonlinearity did not improve predictions, suggesting that other sources, such as the decay of masking and the strength of the medial olivocochlear reflex, may mimic off-frequency nonlinearity. Findings from this study suggest that sources of individual differences can play a strong role in behavioral estimates of compression, and these sources should be considered when using forward masking to study cochlear function in individual listeners or across groups of listeners.

Effects of age and hearing loss on overshoot

“…Thus, individual differences in overshoot measured with a broadband noise in NH and OHI listeners appear to be independent of detection thresholds at frequencies remote from the probe frequency. These results suggest the effect of the cochlear amplifier on individual differences in overshoot is small, or is independent of probe thresholds in quiet, as has been suggested in other studies (Moore et al, 1999;Plack et al, 2004;LopezPoveda et al, 2005;Dubno et al, 2007;Jennings et al, 2014). In conclusion, it is unclear the extent to which individual differences in overshoot are related to peripheral processing, central processing, or an interaction between peripheral and central processing.…”

“…Consistent with this interpretation, individual differences in overshoot may be partially due to differences in cochlear compression among listeners. In addition, individual differences in detection efficiency (which is assumed here to be constant for a given listener in the 2-and 198-ms conditions) may interact with cochlear compression (Jennings et al, 2014) or contribute independently to account for individual differences in overshoot.…”

“…Model simulations followed the approach of Jennings and Strickland (2012) and Jennings et al (2014). Briefly, peripheral processing was simulated using the model of Zilany et al (2009), based on cat physiology.…”

“…Detection efficiency (k) was modeled as a constant postcochlear SNR for all predicted thresholds (Oxenham and Moore, 1994;Jennings et al, 2014). Gain reduction was modeled as a decrease in cochlear amplifier gain over the timecourse of the masker.…”

Effect of Contralateral Medial Olivocochlear Feedback on Perceptual Estimates of Cochlear Gain and Compression

“…This is consistent with previous results (Rosengard et al 2005; Poling et al 2012). Jennings et al (2014) simulated a large set of individual TMCs using a mechanistic model of the auditory periphery proposed by Zilany et al (2009). Their simulations suggested that detection efficiency was the primary cause of inter-individual variability in TMCs in normal-hearing subjects.…”