A model of top-down gain control in the auditory system.
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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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Affiliation: Department of Psychology, University of Toronto Mississauga, 3359 Mississauga Rd., Mississauga, ON, L5L 1C6, Canada. bruce.schneider@utoronto.ca
ABSTRACT
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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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Mentions: The present model is a generalization of the one presented in Parker and Schneider (1994). Let x(t) be the input to the linear auditory filter (see Fig. 10). The output of the filter is c*H[x(t)], where H[x(t)] is the linearly filtered version of the signal and c is a dimensional constant that converts sound pressure into its neural equivalent at the output of the filter. If the filter is sufficiently narrow, we can express the output of the filter over a period of T seconds (T = 1/fc) as being essentially equivalent to\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ c*\sqrt {2} {p_x}\cos \left[ {2\pi {f_c}t + {\theta_x}} \right]where\;{p_x} = RMS\left[ {H\left[ {x(t)} \right]} \right] $$\end{document}and θx is the phase of the sine wave that best matches that of H[x(t)] over the T-second period. Figure 11 shows 2 ms of the digitized output of an auditory filter (gamma-tone filter, center frequency = 500 Hz, equivalent rectangular bandwidth = 96 Hz) to a band-limited white noise (bandwidth = 10 kHz, sampling rate = 20 kHz). Figure 11 shows that an arbitrary output can be well matched to a 500-Hz pure tone whose amplitude is and whose phase is θx.Fig. 10 |
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