Human preferences are inherently subjective—individuals have their preferred types of artwork, shoes to wear, and who they find attractive. However, much of what is known about human preference comes from aggregated data that essentially treat idiosyncratic differences as noise. A holistic understanding of human preference requires building individualized models. In this talk, Dr. Albohn will demonstrate that while some judgments exhibit shared patterns across individuals, others are highly idiosyncratic and difficult to statistically model. Using generative artificial intelligence, Dr. Albohn showcases a novel method to visualize and quantify individual differences in perception, creating valid, robust, and photorealistic representations of how people perceive their world. These findings have significant implications for understanding the influence of individual variation on diverse domains, including visual stereotypes, consumer behavior, and clinical interventions.