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

Related in: MedlinePlus

Transformation results for the noise data of Miller [dashed grey line] and the pure-tone data of Viemeister and Bacon [solid red line]. AAverage intensity JND data. B Estimated loudness functions [L(I)] for the stimuli (pedestals). Triangles represent Millers loudness data (I = SL +10 dB), converted to sones (1 sone = 975 LU) from the calculated values of Neely and Allen. C Eq. 1: Estimated transformation of ΔIjnd [pane A] to ΔLjnd. D Eq. 2: Estimated transformation of ΔIjnd [pane A] to (ΔL/Δt)jnd. The two magnitude-of-loudness-change functions in C are not consistent at low levels – there is an offset, but the rate-of-loudness-change functions in D are closer, indicating that the temporal parameters (duration, envelope) of the stimuli represented in D allow the stimuli to be unified. In D, below ∼0.25 sones the functions are approximately zero slope [i.e., (ΔL/Δt)jnd is constant].
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3585315&req=5

pone-0057497-g002: Transformation results for the noise data of Miller [dashed grey line] and the pure-tone data of Viemeister and Bacon [solid red line]. AAverage intensity JND data. B Estimated loudness functions [L(I)] for the stimuli (pedestals). Triangles represent Millers loudness data (I = SL +10 dB), converted to sones (1 sone = 975 LU) from the calculated values of Neely and Allen. C Eq. 1: Estimated transformation of ΔIjnd [pane A] to ΔLjnd. D Eq. 2: Estimated transformation of ΔIjnd [pane A] to (ΔL/Δt)jnd. The two magnitude-of-loudness-change functions in C are not consistent at low levels – there is an offset, but the rate-of-loudness-change functions in D are closer, indicating that the temporal parameters (duration, envelope) of the stimuli represented in D allow the stimuli to be unified. In D, below ∼0.25 sones the functions are approximately zero slope [i.e., (ΔL/Δt)jnd is constant].

Mentions: Fig. 2(a) shows the re-plotted intensity JND data for Miller and Viemeister and Bacon, illustrating the disparity in function shape that must be overcome within our model. Fig. 2(b) shows the loudness functions of intensity for the pedestals of the respective studies, as estimated using the loudness model. In Fig. 2(b), for comparison to the loudness model results, we also show the loudness level data of Miller [4], as converted by Neely and Allen [9] using the loudness function of Fletcher and Munson [7] (I = SL +10 dB [4]; 1 sone = 975 LU). The shape of the loudness function estimated by the loudness model is in good agreement with the loudness level data of Miller, but the loudness model predicts lower absolute thresholds than the data of Miller suggests (see Description of Modeled Experimental Conditions section).


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

Simpson AJ, Reiss JD - PLoS ONE (2013)

Transformation results for the noise data of Miller [dashed grey line] and the pure-tone data of Viemeister and Bacon [solid red line]. AAverage intensity JND data. B Estimated loudness functions [L(I)] for the stimuli (pedestals). Triangles represent Millers loudness data (I = SL +10 dB), converted to sones (1 sone = 975 LU) from the calculated values of Neely and Allen. C Eq. 1: Estimated transformation of ΔIjnd [pane A] to ΔLjnd. D Eq. 2: Estimated transformation of ΔIjnd [pane A] to (ΔL/Δt)jnd. The two magnitude-of-loudness-change functions in C are not consistent at low levels – there is an offset, but the rate-of-loudness-change functions in D are closer, indicating that the temporal parameters (duration, envelope) of the stimuli represented in D allow the stimuli to be unified. In D, below ∼0.25 sones the functions are approximately zero slope [i.e., (ΔL/Δt)jnd is constant].
© Copyright Policy
Related In: Results  -  Collection

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

pone-0057497-g002: Transformation results for the noise data of Miller [dashed grey line] and the pure-tone data of Viemeister and Bacon [solid red line]. AAverage intensity JND data. B Estimated loudness functions [L(I)] for the stimuli (pedestals). Triangles represent Millers loudness data (I = SL +10 dB), converted to sones (1 sone = 975 LU) from the calculated values of Neely and Allen. C Eq. 1: Estimated transformation of ΔIjnd [pane A] to ΔLjnd. D Eq. 2: Estimated transformation of ΔIjnd [pane A] to (ΔL/Δt)jnd. The two magnitude-of-loudness-change functions in C are not consistent at low levels – there is an offset, but the rate-of-loudness-change functions in D are closer, indicating that the temporal parameters (duration, envelope) of the stimuli represented in D allow the stimuli to be unified. In D, below ∼0.25 sones the functions are approximately zero slope [i.e., (ΔL/Δt)jnd is constant].
Mentions: Fig. 2(a) shows the re-plotted intensity JND data for Miller and Viemeister and Bacon, illustrating the disparity in function shape that must be overcome within our model. Fig. 2(b) shows the loudness functions of intensity for the pedestals of the respective studies, as estimated using the loudness model. In Fig. 2(b), for comparison to the loudness model results, we also show the loudness level data of Miller [4], as converted by Neely and Allen [9] using the loudness function of Fletcher and Munson [7] (I = SL +10 dB [4]; 1 sone = 975 LU). The shape of the loudness function estimated by the loudness model is in good agreement with the loudness level data of Miller, but the loudness model predicts lower absolute thresholds than the data of Miller suggests (see Description of Modeled Experimental Conditions section).

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
Related in: MedlinePlus