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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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Block diagram of the central excitation pattern model and rate-of-change detector process.The area indicated as peripheral contains the loudness model of Glasberg and Moore [19] and the area indicated as central contains the proposed additions of the present study.
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pone-0057497-g003: Block diagram of the central excitation pattern model and rate-of-change detector process.The area indicated as peripheral contains the loudness model of Glasberg and Moore [19] and the area indicated as central contains the proposed additions of the present study.

Mentions: A general block diagram of the proposed central excitation pattern model and rate-of-change detector is given in Fig. 3. Glasberg and Moore [19] provided a loudness model that operates on the temporal waveform of a given sound to produce a time-dependent loudness function. We extend this model to produce a time-dependent central loudness contrast function which can be used to predict those changes in the intensity of a sound that may be detectable. It should be noted that our definition of central loudness (change) is purely functional/notational, in order to maintain some consistency with the previous literature regarding the loudness JND.


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

Simpson AJ, Reiss JD - PLoS ONE (2013)

Block diagram of the central excitation pattern model and rate-of-change detector process.The area indicated as peripheral contains the loudness model of Glasberg and Moore [19] and the area indicated as central contains the proposed additions of the present study.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0057497-g003: Block diagram of the central excitation pattern model and rate-of-change detector process.The area indicated as peripheral contains the loudness model of Glasberg and Moore [19] and the area indicated as central contains the proposed additions of the present study.
Mentions: A general block diagram of the proposed central excitation pattern model and rate-of-change detector is given in Fig. 3. Glasberg and Moore [19] provided a loudness model that operates on the temporal waveform of a given sound to produce a time-dependent loudness function. We extend this model to produce a time-dependent central loudness contrast function which can be used to predict those changes in the intensity of a sound that may be detectable. It should be noted that our definition of central loudness (change) is purely functional/notational, in order to maintain some consistency with the previous literature regarding the loudness JND.

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