Hallucinations Research in a Time of Crisis
Bibliographic record
Abstract
Across the globe, the coronavirus pandemic has had deep and persistent impacts on mental as well as physical health. Hallucinations researchers must work together to address a number of urgent new challenges, including to understand the effects of lockdown and social isolation on susceptibility to unusual experiences such as auditory and visual hallucinations, particular vulnerabilities to such experiences among older people, and non-veridical experiences of loved ones who—tragically and increasingly as a result of this terrible virus—are no longer there. The International Consortium on Hallucination Research (ICHR) was founded in 2011 with a view to facilitating collaboration among hallucinations researchers worldwide. Over 2 sunny days in September 2019, the biennial meeting of the Consortium was held in Durham, United Kingdom, with the generous support of the Wellcome Trust. Sixty-eight delegates from 16 different countries attended the meeting, which was organized around the collaborations of 14 working groups addressing different aspects of hallucinations research. Working groups were at various stages of development, with several planning to report more fully at the ICHR biennial meeting scheduled for Groningen, the Netherlands, in 2021. This Invited Theme presents findings from 4 of the 2019 working groups. Kamp et al’s multidisciplinary team1 investigates hallucinations and other unusual sensory experiences in individuals who have been bereaved, highlighting phenomenology, culture, and theoretical models, as well as considering relations between sensory and quasi-sensory “experiences of the deceased” and hallucinations in psychosis and other psychiatric disorders. Badcock et al2 present a practical review of hallucinations in older adults, including recommendations for professional training and practice. Leptourgos et al3 take an interdisciplinary approach in integrating research on hallucinations experienced under the influence of psychedelics with those occurring in schizophrenia. Finally, Humpston et al4 examine the use of real-time functional magnetic resonance imaging (fMRI) neurofeedback in relieving distressing auditory verbal hallucinations, making recommendations for methodological advances and future empirical research. As this theme was being assembled, we were deeply saddened to learn of the untimely death of Martin Fortier-Davy, a contributor to this collection. Martin was an outstanding and widely collaborative young scholar of philosophy and anthropology who had already made substantial contributions to the fields of hallucinations, psychedelics studies, and the diversity of consciousness. At the time of his final illness, he had been about to defend his PhD in cognitive sciences before a planned move to the University of California, Berkeley, for a postdoctoral fellowship from the Fyssen Foundation. This Invited Theme is dedicated to his memory.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".