Jiayue Liu1, Josh Stohl2, Enrique Lopez-Poveda3, Tobias Overath1
1 Department of Psychology & Neuroscience, Duke University, Durham, US
2 North American Research Laboratory, MED-EL Corporation, Durham, US
3 Instituto de Neurociencias de Castilla y León, Universidad de Salamanca, Salamanca, Spain
‘Hidden hearing loss’ has inspired a wealth of research, including what and how morphological changes in the auditory periphery might cause this phenomenon. For example, the stochastic undersampling model (Lopez-Poveda & Barrios, 2013) suggests that auditory deafferentation can potentially introduce internal noise in the subsequent auditory processing stages. In this model, auditory fibers (AF) are modelled as samplers, which sample the input sound at individual stochastic rates, and the loss of AFs is mimicked by reducing the number of samplers. However, the parameters used in this model do not capture the full complexity of physiological response characteristics, thus leaving unclear the quantity of information conveyed by the AFs. In our study, half-wave rectification, refractoriness, and three types of AFs are added to the original model to explicitly model AF (type) loss within a more realistic physiological setting. In addition, an artificial-neural-network-based stimulus reconstruction is used to decode the modelled AF responses back to an audio signal (Akbari et al., 2019, Morise et al., 2016). We conducted a pure tone in noise (PTiN) detection task and a modified version of HINT (Nilsson et al., 1994) via MTurk. The behavioral stimuli were degraded using our model, with 3 levels of AF loss (0, 90, 95%). Preliminary results indicate that the PTiN threshold increases significantly with a decrease in the number of fibers, at a rate that aligns well with predictions from Oxenham (2016). For the HINT, the results only showed a significant threshold shift between the 90% and 95% AF loss conditions. In conclusion, our model combines detailed physiological response properties with the stochastic undersampling model and thereby enables more realistic artificial introductions of lesions in the peripheral auditory pathway (e.g. selective frequency loss, or fiber type loss) and thus can benefit the study of auditory pathology for improving hearing restoration devices.