What Is the Link Between Hallucinations, Dreams, and Hypnagogic–Hypnopompic Experiences?
Bibliographic record
Abstract
By definition, hallucinations occur only in the full waking state. Yet similarities to sleep-related experiences such as hypnagogic and hypnopompic hallucinations, dreams and parasomnias, have been noted since antiquity. These observations have prompted researchers to suggest a common aetiology for these phenomena based on the neurobiology of rapid eye movement (REM) sleep. With our recent understanding of hallucinations in different population groups and at the neurobiological, cognitive and interpersonal levels, it is now possible to draw comparisons between the 2 sets of experiences as never before. In the current article, we make detailed comparisons between sleep-related experiences and hallucinations in Parkinson's disease, schizophrenia and eye disease, at the levels of phenomenology (content, sensory modalities involved, perceptual attributes) and of brain function (brain activations, resting-state networks, neurotransmitter action). Findings show that sleep-related experiences share considerable overlap with hallucinations at the level of subjective descriptions and underlying brain mechanisms. Key differences remain however: (1) Sleep-related perceptions are immersive and largely cut off from reality, whereas hallucinations are discrete and overlaid on veridical perceptions; and (2) Sleep-related perceptions involve only a subset of neural networks implicated in hallucinations, reflecting perceptual signals processed in a functionally and cognitively closed-loop circuit. In summary, both phenomena are non-veridical perceptions that share some phenomenological and neural similarities, but insufficient evidence exists to fully support the notion that the majority of hallucinations depend on REM processes or REM intrusions into waking consciousness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".