Decomposing dynamical subprocesses for compositional generalization

Our experiences of the world usually reflect the interaction of multiple dynamical subprocesses. For example, clothes' appearance follows a complicated trajectory that is composed of factors such as becoming dirtier with wear and the colors fading slowly over time. In principle, decomposing these subprocesses can enhance learning efficiency, reduce memory requirements, and facilitate compositional reuse in new environments. This is because identical subprocesses can appear in other contexts, for example, discovering that colors in printed photographs also fade over time. Here, we combined a novel sequence learning task with computational modeling to test whether humans (N = 238) extract subprocesses from their holistic experiences, abstract these away from mere sensory experience, and efficiently recompose this knowledge to solve new problems. In a prior learning phase, two groups of participants were each exposed to sequences of compound images drawn from the product space of two graphs: G1 and G2 for group 1, G3 and G4 for group 2. Subsequently, in a transfer learning phase, all participants experienced compound images that were the product of G1 and G3 but composed of entirely new images. We found that knowledge of subprocesses transferred between tasks such that in a new task environment each group made more accurate predictions pertaining to the structure they had experienced in prior learning. Computational models utilizing predictive representations, based solely on the temporal contiguity of experienced task states, could not explain these data. Instead, behavior was consistent with a model performing structural inference over a hypothesis space of graph structures. Our results provide support for the idea that humans discover and abstract subprocesses from dynamic environments and reuse this knowledge to meet the demands of new environments in an efficient, resource-rational manner.