Towards a dual learning system in humans: a novel RLDDM approach to sensory and category Information

The role of striatal dopaminergic activity is being expanded from signaling reward prediction errors (RPEs) to also regulating inference learning. We propose that the striatum strategically modulates the balance between recency and primacy biases in decision making via adjustments in integration timescale and the exploration-exploitation index. Concurrently, recency and primacy have been shown to depend on the ratio of sensory information (stimulus perceptual strength; SI) vs category information (frequency with which pieces of evidence favor a response option; CI). Hence, we explored how SI and CI may interact with reinforcement dynamics towards efficient learning. We conducted a visual decision-making study with 105 healthy students. Participants identified the dominant color in sequences of 10 static dot clouds (red or blue) and rated their confidence. Virtual coins were rewarded if correct and lost if too slow. We manipulated SI (difference of red - blue dots) and CI (ratio of red/blue frames). Analyses included reverse correlation to compute integration kernels. We designed a new reinforcement learning drift diffusion model (RLDDM) whose parameters varied depending on SI and CI levels. Recency increased with the size of the SI-CI difference, total information (SI+CI) and accuracy. In our RLDDM, the SI-CI difference signals the external vs internal noise balance, thereby modulating discount rate and inverse temperature. Conversely, SI+CI regulates inference updates by affecting learning rate and drift rate. These findings reveal how the relation between statistical traits of evidence might drive online adjustments of perception and reinforcement mechanisms to support learning.