Collecting behavioral data efficiently is a significant challenge faced by many auditory scientists, especially those who conduct clinical or developmental research. The prolonged process of data collection is the bottleneck restricting how much information can be gained from a single test subject and how many participants can be included in a clinical study. The long-term goal of the proposed research is to increase the efficiency of behavioral data collection, making individualized estimation of auditory psychophysical models possible. As the first step toward this goal, the estimation of two important psychophysical models will be studied in detail. The two models are the auditory filter model, a model of spectral resolution, and the cochlear input-output function, a model of peripheral nonlinearity. The parameters of these models, such as the auditory-filter bandwidth and the compression ratio of the cochlear input-output function, have been shown to be reliable indicators of cochlear health and can predict supra-threshold listening deficits. Classical procedures to fit these models use threshold-based approaches: multiple thresholds are measured, and the psychophysical model of interest is fitted using those thresholds. For the proposed procedure, a Bayesian algorithm will used to ensure that the stimulus presented on each trial is the stimulus that maximally accelerates the rate of parameter convergence. This parameter-based approach allows the estimation of the auditory filter or the cochlear input-output function using a single experimental track and fewer than 200 trials. This is approximately ten times faster than procedures currently in use. In the proposed experiments, for both of these models, parameters estimated for normal hearing listeners using the proposed and threshold-based procedures will be compared to determine the relative reliability of the new procedure. The optimal configurations for the new procedure, e.g. how to initiate and terminate an experimental track, will be identified. Additionally, the procedure developed to estimate the auditory filter will be further developed to ensure its suitability for hearing-impaired listeners. Upon the completion of the proposed research program, user-friendly software packages will be made available to hearing research community for the estimation of the auditory filter and the cochlear input-output function. The outcome of this research is expected to have a strong and sustained impact on behavioral studies of hearing and hearing impairment. With the procedures to be developed, the fitting of fundamental auditory models for individual test subjects can become routine. This will open the door to a better understanding of the individual differences in hearing capability because scientists will be able to test more participants and/or make more measurements in their experiments. Moreover, given the efficiency of the procedures, it will be much easier for the future experimenters to track a listener's hearing characteristics longitudinally.
A novel Bayesian adaptive approach that directly estimates the parameters of two psychophysical models indicating frequency selectivity and peripheral compression will be developed to reduce, by a factor of ten, the time needed to fit these models relative to current methods. These models have contributed to our understanding of the difficulties in sound processing faced by hearing-impaired listeners and have been used for the adjustment of assistive listening devices. By reducing the time needed to estimate these models, a rapid, yet comprehensive, evaluation of a listener's hearing status that far exceeds that provided by the audiogram will be available.
|Baltzell, Lucas S; Horton, Cort; Shen, Yi et al. (2016) Attention selectively modulates cortical entrainment in different regions of the speech spectrum. Brain Res 1644:203-12|
|Shen, Yi; Dai, Wei; Richards, Virginia M (2015) A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure. Behav Res Methods 47:13-26|
|Shen, Yi; Manzano, Nicole K; Richards, Virginia M (2015) Psychometric functions for sentence recognition in sinusoidally amplitude-modulated noises. J Acoust Soc Am 138:3613-24|
|Shen, Yi (2014) Gap detection and temporal modulation transfer function as behavioral estimates of auditory temporal acuity using band-limited stimuli in young and older adults. J Speech Lang Hear Res 57:2280-92|
|Shen, Yi; Sivakumar, Rajeswari; Richards, Virginia M (2014) Rapid estimation of high-parameter auditory-filter shapes. J Acoust Soc Am 136:1857-68|