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Different computational models have been developed to study the interaural time difference (ITD) perception. However, only few have used a physiologically inspired architecture to study ITD discrimination. Furthermore, they do not include aspects of hearing impairment. In this work, a framework was developed to predict ITD thresholds in listeners with normal and impaired hearing. It combines the physiologically inspired model of the auditory periphery proposed by Zilany, Bruce, Nelson, and Carney [(2009). J. Acoust. Soc. Am. 126(5), 2390–2412] as a front end with a coincidence detection stage and a neurometric decision device as a back end. It was validated by comparing its predictions against behavioral data for narrowband stimuli from literature. The framework is able to model ITD discrimination of normal-hearing and hearing-impaired listeners at a group level. Additionally, it was used to explore the effect of different proportions of outer- and inner-hair cell impairment on ITD discrimination.

Cochlear implants (CI) stimulate the auditory nerve (AN) with a train of symmetric biphasic current pulses comprising of a cathodic and an anodic phase. The cathodic phase is intended to depolarize the membrane of the neuron and to initiate an action potential (AP) and the anodic phase to neutralize the charge induced during the cathodic phase. Single-neuron recordings in cat auditory nerve using monophasic electrical stimulation show, however, that both phases in isolation can generate an AP. The site of AP generation differs for both phases, being more central for the anodic phase and more peripheral for the cathodic phase. This results in an average difference of 200 μs in spike latency for AP generated by anodic vs cathodic pulses. It is hypothesized here that this difference is large enough to corrupt the temporal coding in the AN. To quantify effects of pulse polarity on auditory perception of CI listeners, a model needs to incorporate the correct responsiveness of the AN to anodic and cathodic polarity. Previous models of electrical stimulation have been developed based on AN responses to symmetric biphasic stimulation or to monophasic cathodic stimulation. These models, however, fail to correctly predict responses to anodic stimulation. This study presents a model that simulates AN responses to anodic and cathodic stimulation. The main goal was to account for the data obtained with monophasic electrical stimulation in cat AN. The model is based on an exponential integrate-and-fire neuron with two partitions responding individually to anodic and cathodic stimulation. Membrane noise was parameterized based on reported relative spread of AN neurons. Firing efficiency curves and spike-latency distributions were simulated for monophasic and symmetric biphasic stimulation. The simulations were in line with the average data for firing thresholds and spike latencies for both, monophasic anodic and monophasic cathodic stimulation. The model also correctly predicted t

Performance in pitch discrimination tasks is limited by variability intrinsic to listeners which may arise from peripheral auditory coding limitations or more central noise sources. Perceptual limitations may be characterized by measuring an observer’s change in performance when introducting external noise in the physical stimulus (Lu and Dosher, 2008). The present study used this approach to attempt to quantify the “internal noise” involved in pitch coding of harmonic complex tones by estimating the amount of harmonic roving required to impair pitch discrimination performance. It remains a matter of debate whether pitch perception of natural complex sounds mostly relies on either spectral excitation-based information or temporal periodicity information. Comparing the way internal noise affects the internal representations of such information to how it affects pitch discrimination performance may help clarify pitch coding mechanisms. As training on frequency discrimination tasks has been found to result in a reduction of internal noise (Jones et al., 2013), it was also investigated whether the effect of harmonic roving varied with musical training

Anecdotally, normal hearing listeners can attend to a single sound source in the presence of other sound sources by forming auditory objects. This is commonly referred to as the cocktail party effect. It is known that listeners use, among others, interaural disparities in time and intensity (referred to as ITD and ILD, respectively) to localize a sound source. An open question is, however, how ITD and ILD information is integrated over frequency, and how streaming affects auditory object formation using interaural disparities. ITD weighting functions were previously derived using inverted sensitivity thresholds of narrowband signals (Stern et al., 1988). This method does not take binaural interference (McFadden and Pasanen, 1976) into account and might not be applicable to more realistic broadband signals.

Cochlear implants (CI) directly stimulate the auditory nerve (AN), bypassing the mechano-electrical transduction in the inner ear. Trains of biphasic, charge balanced pulses (anodic and cathodic) are used as stimuli to avoid damage of the tissue. The pulses of either polarity are capable of producing action potentials (AP) whereby the sites of initiation of the AP differ for the two polarities . A cathodic pulse triggers an AP in the peripheral axon, whereas an anodic pulse triggers an AP in the central axon. The latency difference between the APs initiated at the different sites is about 200μs, which is large enough to affect the temporal coding of sounds and hence, potentially, the communication abilities of the CI listener. In the present study, two recently proposed models of electric stimulation of the AN [1,2] were considered in terms of their efficacy to predict the spike timing for anodic and cathodic stimulation of the AN of cat [3]. The models’ responses to the electrical pulses of various shapes [4,5,6] were also analyzed. It was found that, while the models can account for the firing rates in response to various biphasic pulse shapes, they fail to correctly describe the timing of AP in response to monophasic pulses. Strategies for improving the model performance with respect to correct AP timing are discussed.

In a series of studies, 6 subjects with normal hearing (NH) and 3 with sensorineural hearing loss (SNHL) judged the overall loudness of 6-tone complexes comprised of octave frequencies from 0.25 to 8 kHz. In two tasks, tones were equated in level in dB SPL or in sensation level (SL) and a range of SPL or SL values was tested. Both tasks used two-interval forced-choice trials, with level “jitter” introduced by selecting the level of each tone from a normal distribution with specified mean level and standard deviation of 5 dB. Subjects were instructed to indicate which complex was louder. In the “loudness” task there was no difference in mean level across the two intervals. In the “sample discrimination” task, the two complexes differed by an average of 5 dB. For both tasks, perceptual weights were derived by correlating the differences in level between matched-frequency tones in the complexes and the loudness decision on each trial. Weights derived from the loudness task (no mean level difference) were highly correlated with weights derived from the sample discrimination task (5-dB difference). For SPL conditions, both NH and SNHL subjects placed less weight on the lowest frequency and greater weight on higher frequencies with increasing intensity of the complexes, with larger effects for NH subjects. This effect was not observed in conditions where levels were equated in SL. Weights derived from a single-interval categorical loudness scaling task, where subjects judged the overall loudness of 6-tone complexes, were highly correlated with those from the other tasks. Simulation of these experiments using a model of loudness perception [Moore and Glasberg, J. Hear Res. 188 (70-88)] yielded weights for these stimuli that were highly correlated with specific loudness, but the observed weights did not agree with predicted weights. This suggests that model assumptions regarding specific loudness are not correct.

Weighting patterns for loudness obtained using the reverse correlation method are thought to reveal the relative contributions of different frequency regions to total loudness, the equivalent of specific loudness. Current models of loudness assume that specific loudness is determined by peripheral processes such as compression and masking. Here we test this hypothesis using 20-tone harmonic complexes (200Hz f0, 200 to 4000Hz, 250 ms, 65 dB/Component) added in opposite phase relationships (Schroeder positive and negative). Due to the varying degree of envelope modulations, these time-reversed harmonic complexes have been shown to produce different outputs at the basilar membrane and different amounts of forward and simultaneous masking. The perceptual weights for loudness did not differ for these two complexes. To determine whether the level rove introduced to obtain weights had changed the fundamental differences in the stimuli, a similar level rove (68 dB) was introduced on each component of Schroeder positive and negative forward maskers. The Schroder negative maskers continued to be more effective. These results suggest that perceptual weights for loudness are not completely determined by peripheral processes and reflect a central frequency weighting template.

Temporal integration of loudness of 1 kHz tones with 5 and 200 ms durations was assessed in four subjects using two loudness measurement procedures: categorical loudness scaling (CLS) and loudness matching. CLS provides a reliable and efficient procedure for collecting data on the temporal integration of loudness and previously reported nonmonotonic behavior observed at mid-sound pressure level levels is replicated with this procedure. Stimuli that are assigned to the same category are effectively matched in loudness, allowing the measurement of temporal integration with CLS without curve-fitting, interpolation, or assumptions concerning the form of the loudness growth function.