The Dynamic Range Paradox: A Central Auditory Model of Intensity Change Detection

Abstract

In this paper we use empirical loudness modeling to explore a perceptual sub-category of the dynamic range problem of auditory neuroscience. Humans are able to reliably report perceived intensity (loudness), and discriminate fine intensity differences, over a very large dynamic range. It is usually assumed that loudness and intensity change detection operate upon the same neural signal, and that intensity change detection may be predicted from loudness data and vice versa. However, while loudness grows as intensity is increased, improvement in intensity discrimination performance does not follow the same trend and so dynamic range estimations of the underlying neural signal from loudness data contradict estimations based on intensity just-noticeable difference (JND) data. In order to account for this apparent paradox we draw on recent advances in auditory neuroscience. We test the hypothesis that a central model, featuring central adaptation to the mean loudness level and operating on the detection of maximum central-loudness rate of change, can account for the paradoxical data. We use numerical optimization to find adaptation parameters that fit data for continuous-pedestal intensity change detection over a wide dynamic range. The optimized model is tested on a selection of equivalent pseudo-continuous intensity change detection data. We also report a supplementary experiment which confirms the modeling assumption that the detection process may be modeled as rate-of-change. Data are obtained from a listening test (N = 10) using linearly ramped increment-decrement envelopes applied to pseudo-continuous noise with an overall level of 33 dB SPL. Increments with half-ramp durations between 5 and 50,000 ms are used. The intensity JND is shown to increase towards long duration ramps (p<10−6). From the modeling, the following central adaptation parameters are derived; central dynamic range of 0.215 sones, 95% central normalization, and a central loudness JND constant of 5.5×10−5 sones per ms. Through our findings, we argue that loudness reflects peripheral neural coding, and the intensity JND reflects central neural coding.

Tuning of Human Modulation Filters Is Carrier-Frequency Dependent

“…The prevailing experimental paradigm for assessing the intensity JND specifies a fixed listening level and an adaptively-varied increment size [24–27]. However, due to individual differences in auditory physiology, small changes in listening level produce large changes in the size of the intensity JND (e.g., [28]) and, near threshold, the mapping is both extremely nonlinear and highly individualized.…”

“…In previous studies [21–23], listening levels were fixed relative to the absolute threshold (i.e., sensation level – SL) for each listener in order to provide comparison between intensity JNDs at different carrier frequencies. This resulted in the observation of carrier-frequency dependence in the JND as a function of SL but the findings were not related to temporal integration (see below), a major topic of more recent investigations [24–27]. Here, we invert the traditional experimental paradigm [24–27] such that listening level is adaptively varied and the size of the increment is held constant (see Figure 1).…”

“…This resulted in the observation of carrier-frequency dependence in the JND as a function of SL but the findings were not related to temporal integration (see below), a major topic of more recent investigations [24–27]. Here, we invert the traditional experimental paradigm [24–27] such that listening level is adaptively varied and the size of the increment is held constant (see Figure 1). This normalizes between-subject variance caused by individual differences in absolute thresholds.…”

“…By assessing JNDs at different ramp durations, a modulation-rate-sensitivity function is produced [24–27], characterizing the relative sensitivity of the modulation filter to different ramp (i.e., modulation) rates. From this function, tuning for the modulation filter at each carrier frequency can be estimated.…”

“…However, these findings have not been systematically verified or related to cortical processing of stimulus contrast. More recent studies have suggested a key role of contrast in detecting changes in sound intensity [24–27]. Here, we investigated modulation filters using a novel behavioral method derived from psychoacoustics.…”