Predictive coding, multisensory integration, and attentional control: A multicomponent framework for lucid dreaming.

Lucid dreaming occurs when a person realizes they are dreaming while still asleep, often involving vivid images and unusual bodily sensations like flying. A new multicomponent framework proposes that lucid dreaming arises from prediction error signals during sleep, which are resolved by creating a superordinate self-model that integrates ambiguous sensory inputs from both the body and higher brain regions. Multisensory integration maintains lucidity and contributes to kinesthetic experiences, while attentional control balances top-down mental models and bottom-up sensory precision. This framework links neural correlates of lucid dreaming to sleep and arousal regulation, generating testable predictions about individual differences and neurocognitive mechanisms that induce lucid dreams.

Study at a glance

Abstract

Lucid dreaming (LD) is a mental state in which we realize not being awake but are dreaming while asleep. It often involves vivid, perceptually intense dream images as well as peculiar kinesthetic sensations, such as flying, levitating, or out-of-body experiences. LD is in the cross-spotlight of cognitive neuroscience and sleep research as a particular case to study consciousness, cognition, and the neural background of dream experiences. Here, we present a multicomponent framework for the study and understanding of neurocognitive mechanisms and phenomenological aspects of LD. We propose that LD is associated with prediction error signals arising during sleep and occurring at higher or lower levels of the processing hierarchy. Prediction errors are resolved by generating a superordinate self-model able to integrate ambiguous stimuli arriving from sensory periphery and higher-order cortical regions. While multisensory integration enables lucidity maintenance and contributes to peculiar kinesthetic experiences, attentional control facilitates multisensory integration by dynamically regulating the balance between the influence of top-down mental models and the precision weighting of bottom-up sensory inputs. Our novel framework aims to link neural correlates of LD with current concepts of sleep and arousal regulation and provide testable predictions on interindividual differences in LD as well as neurocognitive mechanisms inducing lucid dreams.