Ward Lawrence M, Neiman Alexander, Moss Frank
Department of Psychology and The Brain Centre, University of British Columbia, Vancouver, Canada.
Biol Cybern. 2002 Aug;87(2):91-101. doi: 10.1007/s00422-002-0328-z.
A recent analysis of the energy detector model in sensory psychophysics concluded that stochastic resonance does not occur in a measure of signal detectability ( d'), but can occur in a percent-correct measure of performance as an epiphenomenon of nonoptimal criterion placement [Tougaard (2000) Biol Cybern 83: 471-480]. When generalized to signal detection in sensory systems in general, this conclusion is a serious challenge to the idea that stochastic resonance could play a significant role in sensory processing in humans and other animals. It also seems to be inconsistent with recent demonstrations of stochastic resonance in sensory systems of both nonhuman animals and humans using measures of system performance such as signal-to-noise ratio of power spectral densities and percent-correct detections in a two-interval forced-choice paradigm, both closely related to d'. In this paper we address this apparent dilemma by discussing several models of how stochastic resonance can arise in signal detection systems, including especially those that implement a "soft threshold" at the input transform stage. One example involves redefining d' for energy increments in terms of parameters of the spike-count distribution of FitzHugh-Nagumo neurons. Another involves a Poisson spike generator that receives an exponentially transformed noisy periodic signal. In this case it can be shown that the signal-to-noise ratio of the power spectral density at the signal frequency, which exhibits stochastic resonance, is proportional to d'. Finally, a variant of d' is shown to exhibit stochastic resonance when calculated directly from the distributions of power spectral densities at the signal frequency resulting from transformation of noise alone and a noisy signal by a sufficiently steep nonlinear response function. All of these examples, and others from the literature, imply that stochastic resonance is more than an epiphenomenon, although significant limitations to the extent to which adding noise can aid detection do exist.
最近一项对感觉心理物理学中能量探测器模型的分析得出结论，在信号检测能力的衡量指标（d'）中不会出现随机共振，但在正确率衡量指标中可能会出现，这是由于非最优标准放置的附带现象所致[Tougaard（2000），《生物控制论》83：471 - 480]。当推广到一般感觉系统中的信号检测时，这一结论对随机共振可能在人类和其他动物的感觉处理中发挥重要作用这一观点构成了严峻挑战。它似乎也与最近在非人类动物和人类感觉系统中利用系统性能指标（如功率谱密度的信噪比和双间隔强制选择范式中的正确检测百分比，两者都与d'密切相关）所展示的随机共振现象不一致。在本文中，我们通过讨论信号检测系统中随机共振如何产生的几种模型来解决这一明显的困境，特别包括那些在输入变换阶段实现“软阈值”的模型。一个例子涉及根据菲茨休 - 纳古莫神经元的脉冲计数分布参数重新定义能量增量的d'。另一个例子涉及一个泊松脉冲发生器，它接收指数变换后的噪声周期信号。在这种情况下，可以证明在信号频率处表现出随机共振的功率谱密度的信噪比与d'成正比。最后，当直接根据仅由噪声和噪声信号通过足够陡峭的非线性响应函数变换后在信号频率处的功率谱密度分布来计算时，d'的一个变体显示出随机共振。所有这些例子以及文献中的其他例子都表明，随机共振不仅仅是一种附带现象，尽管在添加噪声有助于检测的程度上确实存在重大限制。
