Establishing Personal Identity in Reincarnation: Minds and Bodies Reconsidered
Bibliographic record
Abstract
Little is known about how the minds and bodies of reincarnated agents are represented. In three studies, participants decided which individual, out of multiple contenders, was most likely to be the reincarnation of a deceased person, based upon a single matching feature between the deceased and each of the candidates. While most participants endorsed reincarnation as entailing a new body, they reasoned that candidates with a similar physical mark (e.g., a mole) or a similar episodic autobiographical memory to the deceased, when alive, were more likely than candidates with other physical or psychological based similarities to be the reincarnation of the deceased. As predicted, by increasing the distinctiveness of a matching physical mark and an episodic autobiographical memory, while holding others constant, likelihood judgments for the candidate with the similar distinctive physical mark were significantly higher than candidates with non-distinctive physical marks, but differences between the distinct and general episodic autobiographical memory condition did not reach statistical significance. These findings support the claim that we intuitively represent reincarnated agents as psychologically determined but physically embodied, and that different assumptions underpin the use of physical and psychological features to establish identity in reincarnation contexts.
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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 | 0.003 | 0.010 |
| 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 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".