The ability to learn constructions may be important for the development of a self-organizing architecture for artificial general intelligence. Constructions are structural relations between more specific or more abstract conceptual representations. They can be derived from the processes of alignment, collocations and distributed equivalences. An architecture that integrates in situ grounded representations with cognitive productivity is ideally suited to learn constructions. This paper described such an architecture, based on neuronal assembly structures and neuronal ’blackboards’ for grounded compositional representations. The paper outlines how constructions could be learned in such an architecture and how the architecture could eventually develop into an autonomous self-organizing architecture for artificial general intelligence.