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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 Laplace equal-variance model for a four-alternative absolute identification experiment. Each stimulus j, {1 ≤ j ≤ 4}, gives rise to a Laplace distribution of events along the decision axis. The vertical lines represent the criteria that divide the decision axis into k response regions {1 ≤ k ≤ 4} . The shaded portion is the probability that stimulus 1 will be misidentified as stimulus 3
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Fig1: The Laplace equal-variance model for a four-alternative absolute identification experiment. Each stimulus j, {1 ≤ j ≤ 4}, gives rise to a Laplace distribution of events along the decision axis. The vertical lines represent the criteria that divide the decision axis into k response regions {1 ≤ k ≤ 4} . The shaded portion is the probability that stimulus 1 will be misidentified as stimulus 3

Mentions: Parker et al. (2002) showed that a nonlinear gain-control mechanism was qualitatively consistent with participants’ performance in absolute identification (AI) of auditory intensities. In an AI task, a listener has to identify which of m possible stimuli was presented on a trial. For example, in the baseline condition in Parker et al., the possible stimuli consisted of four 1-kHz tones differing in intensity (25, 30, 35, and 40 dB SPL), and the participant was asked to specify which of the four had been presented on a trial by pressing one of four buttons. In a signal-detection analysis of an AI experiment, it is assumed that the presentation of stimulus j on a trial gives rise to a response effect along a decision axis. Because of variability either in the stimulus or in auditory processes, repeated presentations of stimulus j are assumed to give rise to a distribution of effects along the decision axis having some mean, μ, and standard deviation, σ. In the simplest models of this process, the shape of the distribution and its variance are assumed to be independent of stimulus magnitude. Figure 1 presents an example of how events along the decision axis might be distributed. In this model, the distributions of effects associated with a stimulus are Laplacian in shape and have equal variances. The observer is assumed to establish m - 1 criteria along this decision axis that divide it into m different response regions. When a stimulus is presented on a trial, it is assumed that if it evokes an effect in region k, the listener will identify it as tone k. Parker et al., as well as Gordon and Schneider (2007), found that the AI performance of a group of participants was quantitatively consistent with a model such as that shown in Fig. 1, in which the shape of the underlying distributions was Laplacian. In addition, the locations of the means of these distributions and how they changed with the intensity of the loudest stimulus in the set were qualitatively consistent with a model in which the internal representation of a stimulus along the decision axis was governed by a nonlinear gain-control mechanism.Fig. 1


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

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

The Laplace equal-variance model for a four-alternative absolute identification experiment. Each stimulus j, {1 ≤ j ≤ 4}, gives rise to a Laplace distribution of events along the decision axis. The vertical lines represent the criteria that divide the decision axis into k response regions {1 ≤ k ≤ 4} . The shaded portion is the probability that stimulus 1 will be misidentified as stimulus 3
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3118000&req=5

Fig1: The Laplace equal-variance model for a four-alternative absolute identification experiment. Each stimulus j, {1 ≤ j ≤ 4}, gives rise to a Laplace distribution of events along the decision axis. The vertical lines represent the criteria that divide the decision axis into k response regions {1 ≤ k ≤ 4} . The shaded portion is the probability that stimulus 1 will be misidentified as stimulus 3
Mentions: Parker et al. (2002) showed that a nonlinear gain-control mechanism was qualitatively consistent with participants’ performance in absolute identification (AI) of auditory intensities. In an AI task, a listener has to identify which of m possible stimuli was presented on a trial. For example, in the baseline condition in Parker et al., the possible stimuli consisted of four 1-kHz tones differing in intensity (25, 30, 35, and 40 dB SPL), and the participant was asked to specify which of the four had been presented on a trial by pressing one of four buttons. In a signal-detection analysis of an AI experiment, it is assumed that the presentation of stimulus j on a trial gives rise to a response effect along a decision axis. Because of variability either in the stimulus or in auditory processes, repeated presentations of stimulus j are assumed to give rise to a distribution of effects along the decision axis having some mean, μ, and standard deviation, σ. In the simplest models of this process, the shape of the distribution and its variance are assumed to be independent of stimulus magnitude. Figure 1 presents an example of how events along the decision axis might be distributed. In this model, the distributions of effects associated with a stimulus are Laplacian in shape and have equal variances. The observer is assumed to establish m - 1 criteria along this decision axis that divide it into m different response regions. When a stimulus is presented on a trial, it is assumed that if it evokes an effect in region k, the listener will identify it as tone k. Parker et al., as well as Gordon and Schneider (2007), found that the AI performance of a group of participants was quantitatively consistent with a model such as that shown in Fig. 1, in which the shape of the underlying distributions was Laplacian. In addition, the locations of the means of these distributions and how they changed with the intensity of the loudest stimulus in the set were qualitatively consistent with a model in which the internal representation of a stimulus along the decision axis was governed by a nonlinear gain-control mechanism.Fig. 1

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