Using computational auditory models to predict simultaneous masking data: model comparison

Abstract

In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates [15], [1]. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks [20]. Previous efforts, including our own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often continue to exceed experimentally-measured performance [9], [21]. In this paper, we compare predictions of detection performance across several computational auditory models. We also reconcile some of the previously observed discrepancies by incorporating appropriate signal uncertainty into the optimal detector.

Evaluating Auditory Performance Limits: I. One-Parameter Discrimination Using a Computational Model for the Auditory Nerve

“…This article focuses on extension of the SDT approach to allow the use of computational models to predict psychophysical performance limits for auditory discrimination based on information encoded in the stochastic AN discharge patterns. Several studies have used SDT to relate computational auditory models to psychophysical performance (Dau, Püschel, & Kohlrausch, 1996aDau, Kollmeier, & Kohlrausch, 1997a, 1997bGresham & Collins, 1998;Huettel & Collins, 1999). Dau et al (1996aDau et al ( , 1996bDau et al ( , 1997aDau et al ( , 1997b developed computational models of effective auditory processing with the goal of matching predicted and human performance (i.e., in terms of both absolute values and trends for various stimulus parameters).…”

“…Their auditory models are physiologically motivated but are not intended to describe the processing at speci c locations in the auditory pathway, and therefore are not compared directly to physiological responses. Gresham and Collins (1998) and Huettel and Collins (1999) used SDT to evaluate information loss at different stages of several more physiologically based computational auditory models. Psychophysical performance was limited in their analysis only by the random variability associated with the noise stimulus (i.e., their analysis did not include any form of internal physiological noise).…”

Evaluating Auditory Performance Limits: II. One-Parameter Discrimination with Random-Level Variation

“…However, the internal noise used in their model was not directly related to the known physiological noise that exists in AN bers, and thus was somewhat arbitrary. Huettel and Collins (1999) evaluated the information loss in physiological auditory models that resulted from randomization of phase in a tone detection in noise experiment; however, their analysis did not include internal noise. Our study here extends the analysis in the companion article, which quanti ed performance limits for deterministic discrimination tasks based on Poisson neural discharges, to include the effect of random stimulus variation in a single parameter on psychophysical performance limits.…”

“…Computational neural models can describe more accurate physiological responses to a much wider range of stimuli than analytical models. Previous methods that have combined SDT and computational auditory models to predict psychophysical performance have either not included physiological (internal) noise (e.g., Gresham & Collins, 1998;Huettel & Collins, 1999) or have used arbitrary internal noise that was not directly related to physiological variability (e.g., Dau, Püschel, & Kohlrausch, 1996a, 1996bDau, Kollmeier, & Kohlrausch, 1997a, 1997b. Our companion article in this issue ("Evaluating Auditory Performance Limits: II") describes a general method that extends previous studies that have quanti ed the effects of physiological noise on psychophysical performance using analytical auditory nerve (AN) models (e.g., Siebert, 1968Siebert, , 1970Colburn, 1969Colburn, , 1973 to incorporate the use of computational models; however, the SDT analysis in the companion article was limited to deterministic discrimination experiments.…”

Quantifying the Information in Auditory-Nerve Responses for Level Discrimination

“…Although many people have extended Siebert's work with computations of performance based on more detailed assumptions about peripheral coding (e.g., Goldstein 1980;Delgutte 1987;Viemeister 1988;Winslow and Sachs 1988;Winter and Palmer 1991;Huettel and Collins 1999;Heinz et al 2001a,b;reviewed by Delgutte 1996), most of these studies were essentially computational in nature. The computational approach does not take advantage of mathematical expressions that give insight into the relationship among the various sources of information and parameters of dependence.…”