A neural-counting model incorporating refractoriness and spread of excitation. II. Application to loudness estimation

Abstract

In previous paper [Teich and Lachs, J. Acoust. Soc. Am 66, 1738--1749 (1979)] we demonstrated that an energy-based neural counting model incorporating refractoriness and spread of excitation satisfactorily described the results of pure-tone intensity discrimination experiments. In this paper, we show that the identical linear filter refractoriness model (LFRM) also provides proper results for pure-tone loudness estimation experiments at all stimulus levels. In particular, as the stimulus intensity increases from very low to moderate values, the model predicts that the slope of the intensity discrimination curve will climb from 1/2 toward 1, whereas the slope of the loudness function will gradually decline below 1 in this same region. For sufficiently high values of the stimulus intensity, the slopes calculated from a simplified (crude saturation) version of the model are found to be 1--1/4N for the intensity discrimination curve and 1/2N for the loudness function. The quantity N is the number of poles associated with the tuned-filter characteristic of the individual neural channels; it is the only important free parameter in the model. Appropriate values for N appear to lie between 2 and 4, providing an asymptotic slope for the intensity discrimination curve bounded by 7/8 and 15/16 (the near miss to Weber's Law), and an asymptotic slope for the loudness function bounded by 1/4 and 1/8. The results follow from the assumption that the neural concomitant of loudness is the number of impulses observed on a collection of parallel neural channels during a fixed observation time. Our calculations are supported by Hellman and Zwislocki's [J. Acoust. Soc. Am. 33, 687--694 (1961)] observation of unit slope for the loudness function at low intensities and provide a theoretical foundation, based on spread of excitation, for Stevens' power law at high intensities.

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

“…In Part II [2] we showed that the same linear filter refractoriness model (LFRM) also provided satisfactory results for fitting data obtained in pure-tone loudness estimation experiments. In Part III [3] we considered intensity discrimination and loudness summation for variable-bandwidth noise stimuli and demonstrated once again that the LFRM calculations were in accord with the trends of the data.…”

Acoustic reflex threshold and loudness in patients with unilateral hearing losses

“…3 The effects of a masker on hearing thresholds (left masked hearing level) on the acoustic reflex (middle) and loudness (right) for a representative case with simulated hearing loss tion at the level of acoustic nerve fibers or of the lower brain stem plays an important role as an input eliciting the AR and constituting the basis for loudness at the level of the ART. It is hypothesized that an increase in the spike rate of each fiber and the spread of excitation through the cochlea could play an important role in loudness [5,13,18]. The same kind of mechanism is also suggested as a trigger input of the AR [2,12].…”

“…Ages ranged from 14 to 69 years, with a mean age of 44.5 years. Etiologies of deafness included sudden deafness [13], Meniere's disease [1], acoustic tumors [4], other retrocochlear lesions [3] and idiopathic causes [10].…”

“…This suggests that some common information in the cochlear nerves plays an important role both in producing the loudness and in eliciting the acoustic reflex (AR). The spread of excitation through the cochlear neurons is regarded as one of the possible important factors constituting loudness [5,13,18]. Although each neuron has a limited dynamic range as a sound level is raised, the total activity of cochlear afferent nerves increases through the additional involvement of excited neurons.…”

Dead-time-modified photocount mean and variance for chaotic radiation

“…(4) for the photon-counting detection of chaotic radiation of arbitrary spectrum is straightforward, since the first-order statistics do not depend on the spectrum. It is well-known that for linearly polarized chaotic radiation incident upon a detector substantially smaller than the coherence area, the probability density function for the rate X is given by (17) where X is the average value of X. By using Eq.…”