Traditionally, experimental effects on humans are investigated at the group level. In this work, we present a distributional ``spotlight'' to investigate experimental effects at the individual level. Specifically, we estimate the effects on individuals through the changes in the probability distributions of their experimental data across conditions. We test this approach on Reaction Time (RT) data from 10 individuals in a visual search task, examining the effects of (1) information set sizes and (2) the presence or absence of a target on their processing speed. The changes in individuals' RT distributions are measured using three approaches: (i) direct measurements of distributional changes are compared against the changes captured by two established models of RT: (ii) the ex-Gaussian distribution and (iii) the Drift-Diffusion model. We find that direct measurement of distributional changes provides the clearest view of the effects on individuals and highlights the presence of two sub-groups based on the effects experienced: one that shows neither effect and the other showing only the target-presence effect. Moreover, the intra-individual changes across conditions (i.e., the experimental effects) appear much smaller than the inter-individual differences (i.e., the random effects). Generally, these results highlight the merits of going beyond group means and examining the effects on individuals, as well as the effectiveness of the distributional spotlight in such pursuits.

翻译：传统上，对人类实验效应的研究通常在群体层面进行。本研究提出一种分布“聚焦”方法，用于在个体层面探究实验效应。具体而言，我们通过个体实验数据在不同条件下的概率分布变化来估计其受到的效应。我们在视觉搜索任务中采集的10名个体的反应时数据上测试了该方法，考察了（1）信息集合大小与（2）目标存在与否对其加工速度的影响。个体反应时分布的变化通过三种方法进行测量：（i）分布变化的直接测量结果与两种成熟反应时模型的捕获变化进行比较：（ii）ex-Gaussian分布模型与（iii）漂移扩散模型。研究发现，分布变化的直接测量能最清晰地展现个体所受效应，并揭示出基于效应体验差异的两个亚组：一组未显示任何效应，另一组仅显示目标存在效应。此外，条件间的个体内变化（即实验效应）远小于个体间差异（即随机效应）。总体而言，这些结果凸显了超越群体均值、考察个体层面效应的价值，以及分布聚焦方法在此类研究中的有效性。