Choosing Stimuli Wisely by Using Individual Differences: A Step-by-Step Tutorial for the One-Stimulus-Per-Factor Principle in Vision Science and Optometric Assessment

Abstract

Given that data collection time in vision research and clinical assessment is limited, investigators and clinicians must select stimuli that maximize diagnostic information with minimum redundancy. This article proposes that analyzing individual differences—through factor analysis (FA), principal component analysis (PCA), and multidimensional scaling (MDS)—offers a principled approach. The covariance structure of individual variation reveals the minimum number of independent factors underlying a visual function, and therefore the minimum number of stimuli needed for efficient, non-redundant characterization. I term this the One-Stimulus-Per-Factor Principle: one well-chosen stimulus per factor is both necessary and sufficient. A complementary rule governs discovery: at least two to three stimuli per expected factor are needed to establish the factor structure. Converging evidence from luminance and chromatic contrast sensitivity, stereoscopic disparity, motion, hue scaling, face recognition, perceptual organization, and other domains shows that the dimensionality of individual variation is often lower than dense sampling suggests—and sometimes higher than sparse clinical testing captures. The approach also tests construct validity, revealing whether behavioral measures index the mechanisms investigators assume. These findings carry implications for clinical instruments across contrast sensitivity, color vision, stereopsis, and visual field assessment, many of which either oversample a single factor or undersample the full factor space. The same framework clarifies psychiatric applications—including contrast-sensitivity changes in depression, schizophrenia, and ADHD—by separating retinal-neural deficits from optical contributions such as higher-order aberrations and from attentional lapses, and it extends to oculomotor biomarkers. The article develops the Principle as a step-by-step tutorial, with a worked example.