Abstract
Previous psychophysical studies have shown that a steep detection-threshold-versus-stimulation-rate function (multipulse integration; MPI) is associated with laterally positioned electrodes producing a broad neural excitation pattern. These findings are consistent with steep MPI depending on either a certain width of neural excitation allowing a large population of neurons operating at a low point on their dynamic range to respond to an increase in stimulation rate or a certain slope of excitation pattern that allows recruitment of neurons at the excitation periphery. Results of the current study provide additional support for these mechanisms by demonstrating significantly flattened MPI functions in narrow bipolar than monopolar stimulation. The study further examined the relationship between the steepness of the psychometric functions for detection (d' versus log current level) and MPI. In contrast to findings in monopolar stimulation, current data measured in bipolar stimulation suggest that steepness of the psychometric functions explained a moderate amount of the across-site variance in MPI. Steepness of the psychometric functions, however, cannot explain why MPI flattened in bipolar stimulation, since slopes of the psychometric functions were comparable in the two stimulation modes. Lastly, our results show that across-site mean MPI measured in monopolar and bipolar stimulation correlated with speech recognition in opposite signs, with steeper monopolar MPI being associated with poorer performance but steeper bipolar MPI being associated with better performance. If steeper MPI requires broad stimulation of the cochlea, the correlation between monopolar MPI and speech recognition can be interpreted as the detrimental effect of poor spectral resolution on speech recognition. Assuming bipolar stimulation produces narrow excitation, and MPI measured in bipolar stimulation reflects primarily responses of the on-site neurons, the correlation between bipolar MPI and speech recognition can be understood in light of the importance of neural survival for speech recognition.
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