Modeling the Time-Varying and Level-Dependent Effects of the Medial Olivocochlear Reflex in Auditory Nerve Responses

Smalt, Christopher J.; Heinz, Michael G.; Strickland, Elizabeth

doi:10.1007/s10162-013-0430-z

Cited by 21 publications

(35 citation statements)

References 42 publications

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“…However, as the ipsilateral effect of the MOCR may be either equal to or stronger than the contralateral effect (Lilaonitkul and Guinan, 2009), more gain reduction might be expected with an ipsilateral elicitor. Recent modeling work has enabled the quantification of previous physiological data in the cat in terms of amount of gain reduction in response to ipsilateral noise (Chintanpalli et al, 2012;Smalt et al, 2013). Those results are consistent with over 20 dB of gain reduction in the data sets modeled.…”

Section: Relationship To Previous Results and Caveatssupporting

confidence: 69%

Accounting for nonmonotonic precursor duration effects with gain reduction in the temporal window model

Roverud

Strickland

2014

The Journal of the Acoustical Society of America

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The mechanisms of forward masking are not clearly understood. The temporal window model (TWM) proposes that masking occurs via a neural mechanism that integrates within a temporal window. The medial olivocochlear reflex (MOCR), a sound-evoked reflex that reduces cochlear amplifier gain, may also contribute to forward masking if the preceding sound reduces gain for the signal. Psychophysical evidence of gain reduction can be observed using a growth of masking (GOM) paradigm with an off-frequency forward masker and a precursor. The basilar membrane input/output (I/O) function is estimated from the GOM function, and the I/O function gain is reduced by the precursor. In this study, the effect of precursor duration on this gain reduction effect was examined for on-and off-frequency precursors. With on-frequency precursors, thresholds increased with increasing precursor duration, then decreased (rolled over) for longer durations. Thresholds with off-frequency precursors continued to increase with increasing precursor duration. These results are not consistent with solely neural masking, but may reflect gain reduction that selectively affects on-frequency stimuli. The TWM was modified to include history-dependent gain reduction to simulate the MOCR, called the temporal window model-gain reduction (TWM-GR). The TWM-GR predicted rollover and the differences with on-and off-frequency precursors whereas the TWM did not.

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Section: Relationship To Previous Results and Caveatssupporting

confidence: 69%

Accounting for nonmonotonic precursor duration effects with gain reduction in the temporal window model

Roverud

Strickland

2014

The Journal of the Acoustical Society of America

Self Cite

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“…Other masking models based on the assumption that forward masking results from adaptation in the auditory nerve (e.g. [70–72]) may also provide a good fit to our data. However, such models are usually based on complex signal detection mechanisms that may have complicated the comparison to the simple temporal window model approach.…”

Section: Resultsmentioning

confidence: 69%

Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli

et al. 2016

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Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli.

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“…In contrast, this reflex may be partially dysfunctional in listeners with hearing impairment due to OHC damage (Collet et al 1992) and result in a relatively shallower off-frequency TMC. Previous studies have simulated the MOC reflex using the Zilany et al (Smalt et al 2014). This model and other models (e.g., Aguilar et al 2013) could be used in future studies to test the hypothesis that the MOC reflex contributes to relatively steep off-frequency TMC slopes in some listeners.…”

Section: Sensitivity To F Mmentioning

confidence: 99%

Computational Modeling of Individual Differences in Behavioral Estimates of Cochlear Nonlinearities

Jennings

Ahlstrom

Dubno

2014

JARO

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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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Modeling the Time-Varying and Level-Dependent Effects of the Medial Olivocochlear Reflex in Auditory Nerve Responses

Cited by 21 publications

References 42 publications

Accounting for nonmonotonic precursor duration effects with gain reduction in the temporal window model

Accounting for nonmonotonic precursor duration effects with gain reduction in the temporal window model

Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli

Computational Modeling of Individual Differences in Behavioral Estimates of Cochlear Nonlinearities

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