Fast and Slow Learning in Human-Like Intelligence

In this chapter we review the extent to which rapid one-short learning or fast-mapping exists in human learning. We find that it exists in both children and adults, but that it is almost always accompanied by slow consolidated learning in which new knowledge is integrated with existing knowledge-bases. Rapid learning is also present in a broad range of non-human species, particularly in the context of high reward values. We argue that reward prediction errors guide the extent to which fast or slow learning dominates, and present a Complementary Learning Systems neural network model (CTDL) of cortical/hippocampal learning that uses reward prediction errors to adjudicate between learning in the two systems. Developing human-like artificial intelligence will require implementing multiple learning and inference systems governed by a flexible control system with an equal capacity to that of human control systems.