Limits...
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.

Show MeSH
Loudness versus intensity JND.Miller’s averaged data for loudness (diamonds) and the intensity JND (circles/triangles) for broadband noise for two individual listeners, as a function of sound level (SL). Loudness data (diamonds), presented in log loudness units (LU), are taken from Neely and Allen who converted them from loudness level data of Miller using the loudness function of Fletcher and Munson. Above about 20 dB SL the JND is approximately constant (i.e., Weber’s Law) but loudness increases.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3585315&req=5

pone-0057497-g001: Loudness versus intensity JND.Miller’s averaged data for loudness (diamonds) and the intensity JND (circles/triangles) for broadband noise for two individual listeners, as a function of sound level (SL). Loudness data (diamonds), presented in log loudness units (LU), are taken from Neely and Allen who converted them from loudness level data of Miller using the loudness function of Fletcher and Munson. Above about 20 dB SL the JND is approximately constant (i.e., Weber’s Law) but loudness increases.

Mentions: To illustrate the paradox, Fig. 1 shows a comparison of Miller’s [4] wide-band noise data for the intensity JND and for loudness levels as a function of intensity. Miller’s [4] loudness level data are converted into loudness units (LU), taken from Neely and Allen [9] according to the loudness function of Fletcher and Munson [7], and plotted in log(LU) for comparison to the intensity JND. At medium levels and above, loudness rises while the intensity JND remains almost constant.


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

Simpson AJ, Reiss JD - PLoS ONE (2013)

Loudness versus intensity JND.Miller’s averaged data for loudness (diamonds) and the intensity JND (circles/triangles) for broadband noise for two individual listeners, as a function of sound level (SL). Loudness data (diamonds), presented in log loudness units (LU), are taken from Neely and Allen who converted them from loudness level data of Miller using the loudness function of Fletcher and Munson. Above about 20 dB SL the JND is approximately constant (i.e., Weber’s Law) but loudness increases.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585315&req=5

pone-0057497-g001: Loudness versus intensity JND.Miller’s averaged data for loudness (diamonds) and the intensity JND (circles/triangles) for broadband noise for two individual listeners, as a function of sound level (SL). Loudness data (diamonds), presented in log loudness units (LU), are taken from Neely and Allen who converted them from loudness level data of Miller using the loudness function of Fletcher and Munson. Above about 20 dB SL the JND is approximately constant (i.e., Weber’s Law) but loudness increases.
Mentions: To illustrate the paradox, Fig. 1 shows a comparison of Miller’s [4] wide-band noise data for the intensity JND and for loudness levels as a function of intensity. Miller’s [4] loudness level data are converted into loudness units (LU), taken from Neely and Allen [9] according to the loudness function of Fletcher and Munson [7], and plotted in log(LU) for comparison to the intensity JND. At medium levels and above, loudness rises while the intensity JND remains almost constant.

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