A neural-counting model based on physiological characteristics of the peripheral auditory system. V. Application to loudness estimation and intensity discrimination

The psychophysical properties of a multiple-channel neural-counting model are investigated. Each channel represents a peripheral afferent fiber (or a group of such fibers) and consists of a cascade of signal-processing transformations, each of which has a physiological correlate in the auditory system. The acoustic signal (which may be a pure tone or Gaussian noise) is passed by our mathematical construct through the following series of transformations: an outer- and middle-ear transmission function, an inner-ear multiple-pole linear-filter tuning mechanism, a nonlinear receptor saturation function, and a refractoriness-modified Poisson transduction mechanism (which leads to a sub-Poisson neural spike count). Spontaneous neural activity is independently incorporated into each channel by means of an additive refractoriness-modified Poisson process. A union process at a more distal center in the nervous system is generated by a parallel collection of such channels with a density (in frequency) determined by the cochlear mapping function. The statistics of the union count (in a fixed time) are then processed at a decision center in a manner that depends on the psychophysical paradigm under consideration. This random count number is assumed to contain all of the information for the examples we consider. Our model has been used to calculate psychophysical functions for the following paradigms: pure-tone loudness estimation, pure-tone and variable-bandwidth noise intensity discrimination, and variable-bandwidth noise loudness summation. The theoretical results, which are determined in large part by spread of excitation, are in good agreement with human psychophysical data, provided that the parameters of the theoretical model are appropriately chosen. It has been found that a suitable choice of parameters is both physiologically sensible and self-consistent. As a further indication of the consistency of the model, the same general parametric dependencies as neurophysiological isointensity contours for peripheral afferent fibers in the squirrel monkey are exhibited by the single-channel theoretical count mean, which is calculated as a function of stimulus level and frequency. The single-channel count mean-to-variance ratio is in accord with laboratory data. Finally, the roles of the various components comprising our theoretical system are discussed, and our model is compared with related constructs.