Neural representations supporting generalization under continual learning

Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of neural population activity. However, these observations--which are typically made after learning has converged--demonstrate the product of abstraction, but not the process by which abstract knowledge is learned: how are the inputs from concrete experiences transformed into abstract knowledge, and how do neural circuits perform these operations and relay this knowledge? To address these questions, we developed a factorized model of temporal abstraction that builds on the successor representation. The model disentangles the contributions of different levels of abstract learning--from stimulus-stimulus associations to a generalizable task schema--in the form of a factorized prediction error that relates the change in relational knowledge to a predicted change in representational geometry on each trial. We fit the model to the behavior of human participants performing a context-dependent decision task during fMRI. The model captured the learning dynamics at multiple timescales, including the increasing contribution of generalization as participants transferred abstracted relational knowledge between novel task instances. In fMRI, BOLD activity in hippocampus--where, in past work, abstract knowledge was represented after learning--was increasingly attributed to the acquisition of abstract knowledge based on generalization. A similar temporal pattern was observed in entorhinal cortex, a putative source of low-dimensional structural information, and orbitofrontal cortex (OFC), which may depend on relational knowledge to represent state relationships as a cognitive map that guides choices. Indeed, individual variation in the generalization signal in OFC correlated with behavioral performance on key trials that required relational knowledge. Our findings show how the brain regions previously shown to represent abstract knowledge after learning also support the process of abstraction as it evolves from learning concrete associations to a generalizable schema. Our approach offers a computational framework for disentangling the operations driving abstract learning and probing their neural correlates in the dynamics of representational geometry.