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.