Sleep Strengthens Predictive Sequence Coding.

Predictive-coding theories assume that perception and action are based on internal models derived from previous experience. Such internal models require selection and consolidation to be stored over time. Sleep is known to support memory consolidation. We hypothesized that sleep supports both consolidation and abstraction of an internal task model that is subsequently used to predict upcoming stimuli. Human subjects (of either sex) were trained on deterministic visual sequences and tested with interleaved deviant stimuli after retention intervals of sleep or wakefulness. Adopting a predictive-coding approach, we found increased prediction strength after sleep, as expressed by increased error rates to deviant stimuli, but fewer errors for the immediately following standard stimuli. Sleep likewise enhanced the formation of an abstract sequence model, independent of the temporal context during training. Moreover, sleep increased confidence for sequence knowledge, reflecting enhanced metacognitive access to the model. Our results suggest that sleep supports the formation of internal models which can be used to predict upcoming events in different contexts. To efficiently interact with the ever-changing world, we predict upcoming events based on similar previous experiences. Sleep is known to benefit memory consolidation. However, it is not clear whether sleep specifically supports the transformation of past experience into predictions of future events. Here, we find that, when human subjects sleep after learning a sequence of predictable visual events, they make better predictions about upcoming events compared with subjects who stayed awake for an equivalent period of time. In addition, sleep supports the transfer of such knowledge between different temporal contexts (i.e., when sequences unfold at different speeds). Thus, sleep supports perception and action by enhancing the predictive utility of previous experiences.