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

Abstract

Previous studies have combined analytical models of stochastic neural responses with signal detection theory (SDT) to predict psychophysical performance limits; however, these studies have typically been limited to simple models and simple psychophysical tasks. A companion article in this issue ("Evaluating Auditory Performance Limits: I") describes an extension of the SDT approach to allow the use of computational models that provide more accurate descriptions of neural responses. This article describes an extension to more complex psychophysical tasks. A general method is presented for evaluating psychophysical performance limits for discrimination tasks in which one stimulus parameter is randomly varied. Psychophysical experiments often randomly vary a single parameter in order to restrict the cues that are available to the subject. The method is demonstrated for the auditory task of random-level frequency discrimination using a computational auditory nerve (AN) model. Performance limits based on AN discharge times (all-information) are compared to performance limits based only on discharge counts (rate place). Both decision models are successful in predicting that random-level variation has no effect on performance in quiet, which is the typical result in psychophysical tasks with random-level variation. The distribution of information across the AN population provides insight into how different types of AN information can be used to avoid the influence of random-level variation. The rate-place model relies on comparisons between fibers above and below the tone frequency (i.e., the population response), while the all-information model does not require such across-fiber comparisons. Frequency discrimination with random-level variation in the presence of high-frequency noise is also simulated. No effect is predicted for all-information, consistent with the small effect in human performance; however, a large effect is predicted for rate-place in noise with random-level variation.

On the interpretation of sensitivity analyses of neural responses

“…This conclusion is correct because these psychophysical measurements correspond to the situation where a listener is previously trained to a set of tones that only vary in frequency, and tested with the same set. However it does not necessarily generalize to other tasks that may be more ecologically relevant, for example determining the pitch of a periodic sound ͓but see Heinz et al ͑2001b͒ for level variability͔.…”

Encoding and decoding amplitude-modulated cochlear implant stimuli—a point process analysis

“…It assesses all stimulus information in the AN spike trains. A similar approach was been used by Heinz et al (2001a,b) to investigate how auditory nerve responses limit frequency and intensity discrimination in acoustic hearing.…”

“…Our second goal is to infer what aspects of the AN response are consistent with known psychophysical results—that is, to define a decoding of AN spikes that is consistent with behavior. Our approach is motivated by the studies of Heinz et al (2001a, b), in which signal detection theory was used to test how spike timing and spike count information could explain performance limits in normal hearing listeners. In the context of cochlear implant research, there is also a pragmatic motivation for constructing a neural decoder that is consistent with behavior.…”

“…Signal detection based on the precise timing of AN spikes has been used in a past computational study of the peripheral response to acoustic stimulation to predict just-noticeable-differences in tone frequency and stimulus level (Heinz et al 2001a) and to model a random-level frequency discrimination task (Heinz et al 2001b). Those studies, however, focused on acoustic hearing, and the measure of discriminability used in that model relied on the fact that AN spikes were generated via an inhomogeneous Poisson process.…”

Characterizing the dependence of pure-tone frequency difference limens on frequency, duration, and level

“…Frequency is the primary determinant of pitch. Therefore, psychophysical data concerning the ability of human listeners to discriminate frequency provide important constraints for models of pitch perception: models of pitch perception must be able to account for the remarkably small size of DLFs, and for the way in which these thresholds vary as a function of stimulus parameters such as frequency, duration, and level (e.g., Dai et al , 1995; Freyman and Nelson, 1986; Heinz et al , 2001a;b; Micheyl et al , 1998; Moore and Glasberg, 1989; Sek and Moore, 1995; Siebert, 1970). Thus, the formulation of mathematical models that describe DLFs as a function of frequency, duration, and level appears a useful, and important, endeavor (Freyman and Nelson, 1991; Nelson et al , 1983).…”