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.