Individual differences in hearing-impaired data: Stats, troubles, and approaches

Individual differences in hearing ability might be dominated by subcomponents of hearing loss, e.g., cochlear gain loss, cochlear neuropathy, temporal coding deficits in low/high frequency regions, or combinations of these components. Unfortunately, we can only rely on indirect and hypothesis-driven...

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Published inThe Journal of the Acoustical Society of America Vol. 139; no. 4; p. 2101
Main Author Verhulst, Sarah
Format Journal Article
LanguageEnglish
Published 01.04.2016
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Abstract Individual differences in hearing ability might be dominated by subcomponents of hearing loss, e.g., cochlear gain loss, cochlear neuropathy, temporal coding deficits in low/high frequency regions, or combinations of these components. Unfortunately, we can only rely on indirect and hypothesis-driven objective (e.g., OAE/ABR/EFR) and psychoacoustic threshold metrics that aim to quantify these subcomponents of hearing loss, complicating a straightforward explanation of study results. Because correlations statistics often rely on small listener groups in which each data point could have resulted from different SNRs, metric-specific variability, it is not always clear which correlations are significant and meaningful. Additionally, multiple measures provide a multitude of correlations that should all support the common underlying hypothesis before conclusions can be drawn. In this tutorial, I provide some examples and approaches to more (and less) meaningful correlations based on recently collected objective and psychoacoustic measures in a group of normal and hearing-impaired listeners. Finally, I will introduce how computational model approaches might direct the interpretation of experimental results when several interacting sources of hearing impairment impact outcome measures unexpectedly.
AbstractList Individual differences in hearing ability might be dominated by subcomponents of hearing loss, e.g., cochlear gain loss, cochlear neuropathy, temporal coding deficits in low/high frequency regions, or combinations of these components. Unfortunately, we can only rely on indirect and hypothesis-driven objective (e.g., OAE/ABR/EFR) and psychoacoustic threshold metrics that aim to quantify these subcomponents of hearing loss, complicating a straightforward explanation of study results. Because correlations statistics often rely on small listener groups in which each data point could have resulted from different SNRs, metric-specific variability, it is not always clear which correlations are significant and meaningful. Additionally, multiple measures provide a multitude of correlations that should all support the common underlying hypothesis before conclusions can be drawn. In this tutorial, I provide some examples and approaches to more (and less) meaningful correlations based on recently collected objective and psychoacoustic measures in a group of normal and hearing-impaired listeners. Finally, I will introduce how computational model approaches might direct the interpretation of experimental results when several interacting sources of hearing impairment impact outcome measures unexpectedly.
Author Verhulst, Sarah
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