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, which has been described as sampling from a learned transition structure of the environment, is a candidate mechanism by which the brain could use or even learn such relational information in the service of adaptive behavior. 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 any knowledge about the sequential task structure. To investigate neural replay, we analyzed dynamics of multivariate functional magnetic resonance imaging (fMRI) patterns during short pauses from the ongoing statistical learning task. Evidence for sequential replay consistent with the probabilistic task structure was found in occipito-temporal and sensorimotor cortices during short on-task intervals. 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.


Wittkuhn, L., Krippner, L. M., & Schuck, N. W. (2022). Statistical learning of successor representations is related to on-task replay. bioRxiv. https://doi.org/10.1101/2022.02.02.478787