Lucid Loop: Exploring the Parallels between Immersive Experiences and Lucid Dreaming
Bibliographic record
Abstract
Lucid dreaming is the awareness of being in a dream, allowing dream control and living out fantasies. It also has benefits for growth and well-being. Yet, lucid dreaming is not accessible to most people. So, we created Lucid Loop—a neurofeedback-augmented immersive experience that utilizes AI-enhanced visuals and spatial audio in a virtual reality device for simulating lucid dreaming. We interviewed nine lucid dreamers who tried Lucid Loop and helped us propose design considerations: dreaming allusions, reality checks, focus points with neurofeedback, people in the scene, and immersion. Lucid Loop was like lucid dreaming because of its capacity for emotionality and fluidity between self and environment. Participants also noted several differences where technology might be limited. Lucid Loop appears to accurately simulate lucid dreaming, with implications for enhancing well-being and future applications for lucid dream training. Our research generalizes to technologically-mediated simulations of other emotive or internal experiences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".