How do we model the complexity of social perception? A major methodological problem is that the space of possible variables driving social perceptions is infinitely large, thus posing an insurmountable hurdle for conventional approaches. Here, we describe a set of data-driven methods whose objective is to identify quantitative relationships between high-dimensional variables (e.g., visual images) and behaviors (e.g., perceptual decisions) with as little bias as possible. We focus on social perception of faces, although the methods could be applied to other visual and nonvisual categories. We review two reverse correlation approaches: (a) psychophysical methods based on judgments of images altered with randomly generated noise, where the analysis relates the random variations of the images to judgments; and (b) methods based on judgments of randomly generated faces from a statistical, multidimensional face space model, where the analysis relates the dimensions of the face model to judgments.