Investigating the Brain Processes Underlying an Unusual Visual Experience
Bibliographic record
Abstract
Background. This case study investigated the neural correlates of an unusual visual experience in which an individual constantly perceives highly detailed holographic images overlaid on his visual field and can modulate them to an extent. We named this experience Upsight. Our aim was to assess how the phenomenon may relate or differ from visual mental imagery (VMI such as hyperphantasia), imagination, or visual hallucinations (e.g., Charles Bonnet Syndrome). Method: EEG (64-channels) data were collected while the participant alternated between 30-second trials of Upsight and visual mental imagery (VMI) conditions (200 trials each). We conducted power spectral density (scalp and source levels) as well as source functional connectivity (FC) analyses, as well as robust statistics to test the null hypothesis of an absence of a difference (nonparametric statistics and spatiotemporal cluster corrections). Results: Scalp results revealed that, relative to VMI, the Upsight experience was characterized by strong alpha and delta power decreases (widespread with a peak in posterior regions), and gamma power increase (29-45 Hz) in the right frontal and left posterior regions, supporting increased engagement of cognitive and visual processes. Similarly, after source localization, we observed a strong decrease in both spectral power and FC in the alpha frequency band, in brain areas involved in visual processing, spatial orientation, and sensory integration, reflecting increased cortical activation of these areas and brain networks. Conclusions: Upsight involves heightened engagement and processing in visual and cognitive networks relative to VMI. We discuss the phenomenology and results in relation to VMI, imagination, and visual hallucinations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".