Predictive coding, multisensory integration, and attentional control: A multicomponent framework for lucid dreaming.
Proceedings of the National Academy of Sciences of the United States of America – November 01, 2022
Source: PubMed
Summary
Lucid dreaming (LD) offers a unique insight into consciousness, with 50-80% of individuals experiencing it at least once. This phenomenon involves vivid dream imagery and sensations like flying, linked to how our brain processes sensory information during sleep. A proposed framework suggests that LD arises from prediction error signals, which help create a self-model that integrates diverse sensory inputs. By enhancing attentional control and multisensory integration, this model aims to explain variations in LD experiences and the underlying neurocognitive mechanisms involved.
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.