Human vision is mediated by a complex interconnected network of cortical brain areas that jointly represent visual information. While these areas are increasingly well understood in isolation, their representational relationships remain elusive: what representational content is shared between areas or unique to a specific area? Here we determined representational relationships by developing relational neural control (RNC). RNC generates and explores in silico functional magnetic resonance imaging (fMRI) responses for large amounts of images, finding controlling images that align or disentangle responses across areas, thus indicating their shared or unique representational content. We used RNC to investigate the representational relationships for univariate and multivariate fMRI responses of early- and mid-level visual areas. Quantitatively, a large portion of representational content is shared across areas, and unique representational content increases as a function of cortical distance. Qualitatively, we isolated the visual features determining shared or unique representational content, which changed when controlling univariate or multivariate responses. Closing the empirical cycle, we validated the in silico discoveries on in vivo fMRI responses by presenting the controlling images to an independent set of subjects. Together, this reveals how visual areas jointly represent the world as an interconnected network.