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A model of top-down gain control in the auditory system.

Schneider BA, Parker S, Murphy D - Atten Percept Psychophys (2011)

Bottom Line: There were three 20-session conditions: (1) four soft tones (25, 30, 35, and 40 dB SPL) in the set; (2) those four soft tones plus a 50-dB SPL tone; and (3) the four soft tones plus an 80-dB SPL tone.The results were well described by a top-down, nonlinear gain-control system in which the amplifier's gain depended on the highest intensity in the stimulus set.Individual participants' identification judgments were generally compatible with an equal-variance signal-detection model in which the mean locations of the distribution of effects along the decision axis were determined by the operation of this nonlinear amplification system.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, University of Toronto Mississauga, 3359 Mississauga Rd., Mississauga, ON, L5L 1C6, Canada. bruce.schneider@utoronto.ca

ABSTRACT
To evaluate a model of top-down gain control in the auditory system, 6 participants were asked to identify 1-kHz pure tones differing only in intensity. There were three 20-session conditions: (1) four soft tones (25, 30, 35, and 40 dB SPL) in the set; (2) those four soft tones plus a 50-dB SPL tone; and (3) the four soft tones plus an 80-dB SPL tone. The results were well described by a top-down, nonlinear gain-control system in which the amplifier's gain depended on the highest intensity in the stimulus set. Individual participants' identification judgments were generally compatible with an equal-variance signal-detection model in which the mean locations of the distribution of effects along the decision axis were determined by the operation of this nonlinear amplification system.

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The mean percentage of the asymptotic value of the gain-control parameter q as a function of the maximum stimulus intensity for sets of simulations that did not include gain control (upper solid line) and those that did (lower solid line). Also shown are the 95% confidence limits for these mean functions
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Fig12: The mean percentage of the asymptotic value of the gain-control parameter q as a function of the maximum stimulus intensity for sets of simulations that did not include gain control (upper solid line) and those that did (lower solid line). Also shown are the 95% confidence limits for these mean functions

Mentions: One hundred such functions were generated for the 100 simulations and were used to determine the average function describing how closely q approached its asymptotic value (upper solid line in Fig. 12), as well as to estimate the 95% confidence interval for this function (dotted lines bracketing the upper solid line). Figure 12 indicates that when the simulations did not involve gain control, the parameters of the gain-control function fit to such data reflect the absence of gain-control by indicating that gain does not change substantially with the maximum intensity in the stimulus set.Fig. 12


A model of top-down gain control in the auditory system.

Schneider BA, Parker S, Murphy D - Atten Percept Psychophys (2011)

The mean percentage of the asymptotic value of the gain-control parameter q as a function of the maximum stimulus intensity for sets of simulations that did not include gain control (upper solid line) and those that did (lower solid line). Also shown are the 95% confidence limits for these mean functions
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3118000&req=5

Fig12: The mean percentage of the asymptotic value of the gain-control parameter q as a function of the maximum stimulus intensity for sets of simulations that did not include gain control (upper solid line) and those that did (lower solid line). Also shown are the 95% confidence limits for these mean functions
Mentions: One hundred such functions were generated for the 100 simulations and were used to determine the average function describing how closely q approached its asymptotic value (upper solid line in Fig. 12), as well as to estimate the 95% confidence interval for this function (dotted lines bracketing the upper solid line). Figure 12 indicates that when the simulations did not involve gain control, the parameters of the gain-control function fit to such data reflect the absence of gain-control by indicating that gain does not change substantially with the maximum intensity in the stimulus set.Fig. 12

Bottom Line: There were three 20-session conditions: (1) four soft tones (25, 30, 35, and 40 dB SPL) in the set; (2) those four soft tones plus a 50-dB SPL tone; and (3) the four soft tones plus an 80-dB SPL tone.The results were well described by a top-down, nonlinear gain-control system in which the amplifier's gain depended on the highest intensity in the stimulus set.Individual participants' identification judgments were generally compatible with an equal-variance signal-detection model in which the mean locations of the distribution of effects along the decision axis were determined by the operation of this nonlinear amplification system.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, University of Toronto Mississauga, 3359 Mississauga Rd., Mississauga, ON, L5L 1C6, Canada. bruce.schneider@utoronto.ca

ABSTRACT
To evaluate a model of top-down gain control in the auditory system, 6 participants were asked to identify 1-kHz pure tones differing only in intensity. There were three 20-session conditions: (1) four soft tones (25, 30, 35, and 40 dB SPL) in the set; (2) those four soft tones plus a 50-dB SPL tone; and (3) the four soft tones plus an 80-dB SPL tone. The results were well described by a top-down, nonlinear gain-control system in which the amplifier's gain depended on the highest intensity in the stimulus set. Individual participants' identification judgments were generally compatible with an equal-variance signal-detection model in which the mean locations of the distribution of effects along the decision axis were determined by the operation of this nonlinear amplification system.

Show MeSH