Adaptive learning via surprise gated attractor switching

Abstract

People adjust their use of feedback over time through a process referred to as adaptive learning. We have recently proposed that the underlying mechanisms of adaptive learning are rooted in how the brain organizes time into similarly credited units, which we refer to as latent states. Here we develop a basal ganglia-thalamo-cortical circuit model of this process and show that it captures both the commonalities and heterogeneity in human adaptive learning behavior. Our model learns incrementally through synaptic plasticity in prefrontal-basal ganglia (PFC-BG) connections, but upon observing discordant information, produces thalamocortical reset signals that alter PFC connectivity, driving attractor state transitions that facilitate rapid updating of behavioral policy. We demonstrate that this mechanism can give rise to optimized learning dynamics in the context of either change-points or reversals, and that under reasonable biological assumptions the model is able to generalize efficiently across these conditions, adjusting behavior in a context-appropriate manner. Taken together, our results provide a biologically plausible mechanistic model for adaptive learning that explains existing behavioral data and makes testable predictions about the computational roles of different brain regions in complex learning behaviors.