Learning to Discern the Voices of Gods, Spirits, Tulpas, and the Dead
Bibliographic record
Abstract
There are communities in which hearing voices frequently is common and expected, and in which participants are not expected to have a need for care. This paper compares the ideas and practices of these communities. We observe that these communities utilize cultural models to identify and to explain voice-like events-and that there are some common features to these models across communities. All communities teach participants to "discern," or identify accurately, the legitimate voice of the spirit or being who speaks. We also observe that there are roughly two methods taught to participants to enable them to experience spirits (or other invisible beings): trained attention to inner experience, and repeated speech to the invisible other. We also observe that all of these communities model a learning process in which the ability to hear spirit (or invisible others) becomes more skilled with practice, and in which what they hear becomes clearer over time. Practice-including the practice of discernment-is presumed to change experience. We also note that despite these shared cultural ideas and practices, there is considerable individual variation in experience-some of which may reflect psychotic process, and some perhaps not. We suggest that voice-like events in this context may be shaped by cognitive expectation and trained practice as well as an experiential pathway. We also suggest that researchers could explore these common features both as a way to help those struggling with psychosis, and to consider the possibility that expectations and practice may affect the voice-hearing experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".