Human vision is mediated by a complex interconnected network of cortical brain areas jointly representing visual information. While these areas are increasingly understood in isolation, their representational relationships remain elusive. Here we developed relational neural control (RNC), and used it to investigate the representational relationships for univariate and multivariate fMRI responses of early- and mid-level visual areas. RNC generated and explored in silico fMRI responses for large amounts of images, discovering controlling images that align or disentangle responses across areas, thus indicating their shared or unique representational content. A large portion of representational content was shared across areas, unique representational content increased with cortical distance, and we isolated the visual features determining these effects. Closing the empirical cycle, we validated the in silico discoveries on in vivo fMRI responses from independent subjects. Together, this reveals how visual areas jointly represent the world as an interconnected network.