Abstract concepts are rules about relationships such as identity or sameness. Instead of learning that specific objects belong to specific categories, the abstract concept of same/different applies to any objects that an organism might encounter, even if those objects have never been seen before. In this paper we investigate learning of abstract concepts by computer, in order to recognize same/different in novel data never seen before. To do so, we integrate recursive self-organizing maps with the data they are processing into a single graph to enable a brain-like self- adaptive learning system. We perform experiments on simple same/different datasets designed to resemble those used in animal experiments and then show an example of a practical application of same/different learning using the approach.