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Record W1894837588 · doi:10.1163/15685373-12342158

Establishing Personal Identity in Reincarnation: Minds and Bodies Reconsidered

2015· article· en· W1894837588 on OpenAlexaff
Claire White

Bibliographic record

VenueJournal of Cognition and Culture · 2015
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsQueen's University
Fundersnot available
KeywordsReincarnationPsychologyEmbodied cognitionPhysical bodyIdentity (music)Autobiographical memoryMisattribution of memoryEpisodic memoryPersonal identityOptimal distinctiveness theoryCognitive psychologySocial psychologySelfRecallEpistemologyAestheticsCognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.360
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2015
Admission routes1
Has abstractyes

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