Statistical learning of successor representations is related to on-task replay

Abstract

Humans automatically infer higher-order relationships between events in the environment from their statistical co-occurrence, often without conscious awareness. Neural replay of task representations is a candidate mechanism by which the brain learns such relational information or samples from a learned model in the service of adaptive behavior. Here, we tested whether cortical reactivation is related to learning higher-order sequential relationships without consciousness. Human participants viewed sequences of images that followed probabilistic transitions determined by ring-like graph structures. Behavioral modeling revealed that participants acquired multi-step transition knowledge through gradual updating of an internal successor representation (SR) model, although half of participants did not indicate conscious knowledge about the sequential task structure. To investigate neural replay, we analyzed the temporal dynamics of multivariate functional magnetic resonance imaging (fMRI) patterns during brief 10 seconds pauses from the ongoing statistical learning task. We found evidence for backward sequential replay of multi-step sequences in visual cortical areas. These findings indicate that implicit learning of higher-order relationships establishes an internal SR-based map of the task, and is accompanied by cortical on-task replay.