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Computational Modeling of Individual Differences in Behavioral Estimates of Cochlear Nonlinearities

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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.

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Acknowledgments

This work was supported by grants R01 DC000184 and P50 DC000422 from NIH/NIDCD, the South Carolina Clinical & Translational Research (SCTR) Institute, with an academic home at the Medical University of South Carolina, and NIH/NCRR Grant number UL1 RR029882. This investigation was conducted in a facility constructed with support from Research Facilities Improvement Program Grant Number C06 RR14516 from the National Center for Research Resources, National Institutes of Health. The authors thank the associate editor and two anonymous reviewers for their helpful comments on a previous version of this manuscript.

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Correspondence to Skyler G. Jennings.

APPENDIX

APPENDIX

The best-fitting predictors were not unique in effectively predicting the TMCs of a given subject. In other words, there were other predictor combinations that produced similar rms error as the best-fitting combination. Figure 10 displays contour plots of the rms error from predictions of two representative normal-hearing (Fig. 10A) and hearing-impaired (Fig. 10B) subjects. Each contour plot shows how the rms error varies across changes in the two most sensitive predictors (f m and k for normal-hearing listeners; θ and p OHC for hearing-impaired listeners). For illustration, the remaining predictors were set at the best-fitting predictor values for a given subject (i.e., the values shown in Tables 1 and 2). Regions of low rms error in normal-hearing simulations were often confined to a restricted range of k. This range was quite narrow in subjects who were estimated to have low k (Fig. 10, S19), while subjects with high k showed a broader range (Fig. 10, S11). This suggests that the model’s sensitivity to k is greater in subjects with low k, compared to subjects with high k. In contrast to k, regions of low rms error were broad for f m and often spanned the entire f m axis (columns of blue shading in Fig. 10A). In hearing-impaired simulations, predictions were best for several combinations of θ and p OHC lying on a diagonal path from relatively low θ and p OHC to relatively high θ and p OHC. This suggests that if both θ and p OHC were slightly increased or decreased relative to the best-fitting predictors, TMCs would continue to be well fit by the model. This interaction between θ and p OHC on rms error is due to these predictors having the same general effect when adjusted individually. When holding other predictors constant, TMC thresholds increase as θ increases and as p OHC decreases (i.e., more IHC dysfunction); thus, if a given θ predicts too much or too little hearing loss, p OHC can be adjusted to improve the rms error.

FIG. 10
figure 10

rms contour plots from simulations of two representative normal-hearing (A) and two representative hearing-impaired (B) subjects. Each point of the plot represents the rms error between the measured and predicted temporal masking curves and are shown for all combinations of the most sensitive predictors of a given subject group (k and f m for normal-hearing listeners; p OHC and θ for hearing-impaired listeners). Other predictors were held constant at the values presented in Tables 1 and 2. Lines display regions of the rms value given by the associated numbers. Cool and warm colors represent low and high rms error, respectively.

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Jennings, S.G., Ahlstrom, J.B. & Dubno, J.R. Computational Modeling of Individual Differences in Behavioral Estimates of Cochlear Nonlinearities. JARO 15, 945–960 (2014). https://doi.org/10.1007/s10162-014-0486-4

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