Diagrams are powerful opportunities for grappling with and learning abstract relationships, for example learning the relations between elements in an ecosystem rather than simply memorizing the objects within the system. Further, what is crucial from any diagrammatic learning opportunity is the ability to use this relational knowledge in a new context or with new materials, beyond simply understanding the initial presentation. This is cognitively effortful, however, and individual differences in how reasoners benefit from such relational learning opportunities are not well understood. We describe a computational simulation that examines how cognitive control of attention enables relational learning from visual stimuli such as diagrams. Specifically, we propose that cognitive control is critical for both abstracting relational representations from that visual stimuli, and to the ability to use these representations in subsequent problem solving. [This paper was published in: Chapman P., Stapleton G., Moktefi A., Perez-Kriz S., Bellucci F. (eds) "Diagrammatic Representation and Inference. Diagrams 2018: LNAI 10871" (pp. 821-824). Edinburgh, UK: Springer.]