Trait anxiety is associated with reduced reward-related replay at rest.

Understanding how we learn about the value and structure of our environment is central to neurocognitive theories of many psychiatric and neurological disorders. Learning processes have been extensively studied during performance of behavioural tasks (online learning) but less so in relation to resting (offline) states. A candidate mechanism for such offline learning is replay, the sequential neural reactivation of past experiences. Notably, value-based learning is especially tied to replay unfolding in reverse order relative to the original experience (backward replay). Here, we demonstrate the utility of EEG-based neural decoding for investigating offline learning, and relate it to trait anxiety, measured using the Spielberger Trait Anxiety Inventory. Participants were first required to infer sequential relationships among task objects by using a learned rule to reorganise their visual experiences into distinct sequences. Afterwards, they observed that the final object in one of the sequences was associated with a monetary reward and then entered a post-value resting state. During this rest, we find evidence of backward replay for reward-linked object sequences. The strength of such replay is negatively associated with trait anxiety and positively predicts an increased behavioural preference for reward-predictive stimuli. We also find that healthy individuals with high trait anxiety (score ≥ 45) show inefficient credit assignment irrespective of reward magnitude, indicating that this effect does not merely reflect reduced reward sensitivity. Together, these findings suggest a potential aberrant replay mechanism during offline learning in individuals with high trait anxiety. More broadly, our approach illustrates the potential of EEG for measuring structured neural representations in vivo.