Erell A
Department of Electronic Systems, Faculty of Engineering, Tel Aviv University, Ramat Aviv, Israel.
J Acoust Soc Am. 1988 Jul;84(1):204-14. doi: 10.1121/1.396966.
The predictions of a rate coding model for frequency and amplitude jnd's in the presence of noise are presented for a 1-kHz, 100-ms tone. The model for the neural response incorporates physiological data on dynamic range distribution and rate suppression. A central processor is assumed to estimate the tone frequency, or amplitude, from the tone-evoked rate increment profile. This central processor acts like an ideal detector with respect to the neural noise. The effects of the neural noise as well as the signal variability on the discrimination performance level are evaluated, and the signal variability is found to be significant. The combined effect of threshold distribution, rate suppression, and signal variability make the jnd's practically invariant with noise level, in accordance with published psychophysical data. The values of the frequency jnd at high signal-to-noise ratio, however, are borderline in their consistency with the data. A more obvious discrepancy exists between the model and the psychophysical data regarding the ratio of frequency to amplitude Weber fractions, which can be resolved only by modifying the model auditory filters to be five times sharper than those measured in cats.
针对1千赫兹、100毫秒的纯音，给出了存在噪声时频率和幅度的差别阈限（jnd）的速率编码模型预测。神经反应模型纳入了关于动态范围分布和速率抑制的生理数据。假定有一个中央处理器根据纯音诱发的速率增量分布图来估计纯音频率或幅度。就神经噪声而言，这个中央处理器的作用就像一个理想探测器。评估了神经噪声以及信号变异性对辨别性能水平的影响，发现信号变异性很显著。根据已发表的心理物理学数据，阈值分布、速率抑制和信号变异性的综合作用使得差别阈限实际上与噪声水平无关。然而，高信噪比下频率差别阈限的值与数据的一致性处于临界状态。在频率与幅度韦伯分数之比方面，模型与心理物理学数据之间存在更明显的差异，只有将模型听觉滤波器修改得比在猫身上测得的滤波器尖锐五倍才能解决这一差异。
