Information processing underlying human perceptual decision-making is inherently noisy and identifying sources of this noise is important to understand processing. Ratcliff, Voskuilen, and McKoon (2018) examined results from five experiments using a double-pass procedure in which stimuli were repeated typically a hundred trials later. Greater than chance agreement between repeated tests provided evidence for trial-to-trial variability from external sources of noise. They applied the diffusion model to estimate the quality of evidence driving the decision process (drift rate) and the variability (standard deviation) in drift rate across trials. This variability can be decomposed into random (internal) and systematic (external) components by comparing the double-pass accuracy and agreement with the model predictions. In this note, we provide an additional analysis of the double-pass experiments using the linear ballistic accumulator (LBA) model. The LBA model does not have within-trial variability and thus it captures all variability in processing with its across-trial variability parameters. The LBA analysis of the double-pass data provides model-based evidence of external variability in a decision process, which is consistent with Ratcliff et al.'s result. This demonstrates that across-trial variability is required to model perceptual decision-making. The LBA model provides measures of systematic and random variability as the diffusion model did. But due to the lack of within-trial variability, the LBA model estimated the random component as a larger proportion of across-trial total variability than did the diffusion model.

中文翻译：

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关于感知决策中可变性来源分解的说明


人类感知决策的信息处理本质上是有噪声的，识别这种噪声的来源对于理解处理过程很重要。 Ratcliff、Voskuilen 和 McKoon（2018）使用双遍程序检查了五次实验的结果，其中通常在一百次试验后重复刺激。重复测试之间大于偶然的一致性提供了来自外部噪声源的试验间变异性的证据。他们应用扩散模型来估计驱动决策过程的证据质量（漂移率）以及试验中漂移率的变异性（标准差）。通过比较双通道精度和与模型预测的一致性，可以将这种变异性分解为随机（内部）和系统（外部）分量。在本文中，我们使用线性弹道累加器（LBA）模型对双通道实验进行了额外的分析。 LBA 模型不具有试验内变异性，因此它通过其跨试验变异性参数捕获处理中的所有变异性。双通道数据的 LBA 分析提供了决策过程中外部可变性的基于模型的证据，这与 Ratcliff 等人的结果一致。这表明需要跨试验的变异性来模拟感知决策。与扩散模型一样，LBA 模型提供了系统和随机变异性的测量。但由于缺乏试验内变异性，LBA​​ 模型估计随机成分在试验间总变异性中所占的比例比扩散模型更大。