Department of Psychology.
School of Health and Life Sciences.
Psychol Rev. 2020 Oct;127(5):932-944. doi: 10.1037/rev0000192.
A key assumption of models of human cognition is that there is variability in information processing. Evidence accumulation models (EAMs) commonly assume 2 broad variabilities in information processing: within-trial variability, which is thought to reflect moment-to-moment fluctuations in perceptual processes, and between-trial variability, which is thought to reflect variability in slower-changing processes like attention, or systematic variability between the stimuli on different trials. Recently, Ratcliff, Voskuilen, and McKoon (2018) claimed to "provide direct evidence that external noise is, in fact, required to explain the data from five simple two-choice decision tasks" (p. 33), suggesting that at least some portion of the between-trial variability in information processing is due to "noise." However, we argue that Ratcliff et al. (2018) failed to distinguish between 2 different potential sources of between-trial variability: random (i.e., "external noise") and systematic (e.g., item effects). Contrary to the claims of Ratcliff et al. (2018), we show that "external noise" is not required to explain their findings, as the same trends of data can be produced when only item effects are present. Furthermore, we contend that the concept of "noise" within cognitive models merely serves as a convenience parameter for sources of variability that we know exist but are unable to account for. Therefore, we question the usefulness of experiments aimed at testing the general existence of "random" variability and instead suggest that future research should attempt to replace the random variability terms within cognitive models with actual explanations of the process. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
人类认知模型的一个关键假设是信息处理存在可变性。证据积累模型（EAMs）通常假设信息处理存在 2 种广泛的可变性：试验内变异性，被认为反映了知觉过程中瞬息万变的波动；试验间变异性，被认为反映了注意力等较慢变化过程的变异性，或者不同试验中刺激之间的系统变异性。最近，Ratcliff、Voskuilen 和 McKoon（2018）声称“提供了直接证据，表明实际上需要外部噪声来解释来自五个简单的二选一决策任务的数据”（第 33 页），这表明信息处理中的至少一些试验间变异性是由于“噪声”造成的。然而，我们认为 Ratcliff 等人（2018）未能区分信息处理中试验间变异性的 2 个不同潜在来源：随机（即“外部噪声”）和系统（例如，项目效应）。与 Ratcliff 等人（2018）的说法相反，我们表明“外部噪声”不是解释他们发现的必需条件，因为当只有项目效应存在时，也可以产生相同的数据趋势。此外，我们认为认知模型中“噪声”的概念只是一个方便的参数，用于表示我们知道存在但无法解释的变异性来源。因此，我们质疑旨在测试“随机”变异性一般存在性的实验的有用性，而建议未来的研究应该尝试用实际的过程解释来代替认知模型中的随机变异性术语。（PsycInfo 数据库记录（c）2020 APA，保留所有权利）。
