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The dynamic range paradox: a central auditory model of intensity change detection.

Simpson AJ, Reiss JD - PLoS ONE (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Centre for Digital Music, Queen Mary University of London, London, United Kingdom. andy.simpson@eecs.qmul.ac.uk

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.

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Optimization results; peripheral versus central model.A Central loudness (solid red line) for continuous pedestals, as a function of peripheral loudness (dashed grey line), illustrating the saturating effect of central adaptation (Eq. 3). B, C Comparison of estimated intensity JNDs from the peripheral and central excitation pattern rate models respectively. B circles: the averaged 1-kHz continuous pure-tone increment-detection data of Viemeister and Bacon and C is the individual (circles and triangles) continuous-noise increment-detection data of Miller.
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pone-0057497-g004: Optimization results; peripheral versus central model.A Central loudness (solid red line) for continuous pedestals, as a function of peripheral loudness (dashed grey line), illustrating the saturating effect of central adaptation (Eq. 3). B, C Comparison of estimated intensity JNDs from the peripheral and central excitation pattern rate models respectively. B circles: the averaged 1-kHz continuous pure-tone increment-detection data of Viemeister and Bacon and C is the individual (circles and triangles) continuous-noise increment-detection data of Miller.

Mentions: Fig. 4(a) shows the resulting central loudness (red line) as a function of peripheral loudness (grey, dashed line), illustrating the result of the optimization and the effects of central adaptation. In order to show the effect of central adaptation on the estimated intensity JND functions, Figs. 4(b, c) show the rate-of-change predictions of the unaltered peripheral model (grey, dashed line) compared to the optimized central excitation pattern model (red line) for the data of Viemeister and Bacon (Fig. 4b) and Miller (Fig. 4c). The fit of the optimized central excitation pattern model to the data of Viemeister and Bacon is good (r = 0.99, p = 1.8×10−13, e = 0.04 dB), as is the fit to the data of Miller (r = 0.94, p = 1.4×10−5, e = 0.19 dB). The growth of loudness for both cases (tones/noise) gives a good prediction below central adaptation threshold. However, in both cases, the unaltered peripheral model results diverge strongly from those of the optimized central model above approximately 0.2 sones and the peripheral model fails to hold to the data at higher levels. As can be expected from looking at Fig. 4(b/c), the value of TCA is relatively tightly controlled by the fact that a larger value would increase the error for the data of Viemeister and Bacon (Fig. 4b) and a smaller value would increase the error for the data of Miller (Fig. 4c). The value of alpha is also relatively tightly constrained by the fact that smaller values would cause the functions to tend towards the under-estimation of the peripheral model output, and the fact that for larger values the model would tend towards Weber’s Law for the tonal data.


The dynamic range paradox: a central auditory model of intensity change detection.

Simpson AJ, Reiss JD - PLoS ONE (2013)

Optimization results; peripheral versus central model.A Central loudness (solid red line) for continuous pedestals, as a function of peripheral loudness (dashed grey line), illustrating the saturating effect of central adaptation (Eq. 3). B, C Comparison of estimated intensity JNDs from the peripheral and central excitation pattern rate models respectively. B circles: the averaged 1-kHz continuous pure-tone increment-detection data of Viemeister and Bacon and C is the individual (circles and triangles) continuous-noise increment-detection data of Miller.
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pone-0057497-g004: Optimization results; peripheral versus central model.A Central loudness (solid red line) for continuous pedestals, as a function of peripheral loudness (dashed grey line), illustrating the saturating effect of central adaptation (Eq. 3). B, C Comparison of estimated intensity JNDs from the peripheral and central excitation pattern rate models respectively. B circles: the averaged 1-kHz continuous pure-tone increment-detection data of Viemeister and Bacon and C is the individual (circles and triangles) continuous-noise increment-detection data of Miller.
Mentions: Fig. 4(a) shows the resulting central loudness (red line) as a function of peripheral loudness (grey, dashed line), illustrating the result of the optimization and the effects of central adaptation. In order to show the effect of central adaptation on the estimated intensity JND functions, Figs. 4(b, c) show the rate-of-change predictions of the unaltered peripheral model (grey, dashed line) compared to the optimized central excitation pattern model (red line) for the data of Viemeister and Bacon (Fig. 4b) and Miller (Fig. 4c). The fit of the optimized central excitation pattern model to the data of Viemeister and Bacon is good (r = 0.99, p = 1.8×10−13, e = 0.04 dB), as is the fit to the data of Miller (r = 0.94, p = 1.4×10−5, e = 0.19 dB). The growth of loudness for both cases (tones/noise) gives a good prediction below central adaptation threshold. However, in both cases, the unaltered peripheral model results diverge strongly from those of the optimized central model above approximately 0.2 sones and the peripheral model fails to hold to the data at higher levels. As can be expected from looking at Fig. 4(b/c), the value of TCA is relatively tightly controlled by the fact that a larger value would increase the error for the data of Viemeister and Bacon (Fig. 4b) and a smaller value would increase the error for the data of Miller (Fig. 4c). The value of alpha is also relatively tightly constrained by the fact that smaller values would cause the functions to tend towards the under-estimation of the peripheral model output, and the fact that for larger values the model would tend towards Weber’s Law for the tonal data.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Centre for Digital Music, Queen Mary University of London, London, United Kingdom. andy.simpson@eecs.qmul.ac.uk

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.

Show MeSH