Déjà Vu as Overlap Between Dream Simulations and Waking Experience

This study proposes a novel predictive-processing framework to explain déjà vu as arising from partial overlaps between dream-generated internal simulations and waking experiences. Building on neuroscientific evidence that dreams function as offline generative models during REM sleep, the hypothesis suggests that when waking scenes share sensory, emotional, or contextual features with overnight dream fragments, they activate familiarity circuits, producing the characteristic feeling of "having been here before." To test this, the project will reanalyze existing datasets—such as dream reports, neuroimaging data, and recognition memory studies—using advanced NLP techniques to quantify content similarity, coupled with computational modeling to simulate how these overlaps generate familiarity signals. This approach aims to move beyond traditional theories of déjà vu rooted in memory misattribution or neural misfiring, offering a mechanistic, empirically testable explanation that bridges sleep, dreaming, and perceptual familiarity within a unified predictive-processing framework.