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Record W2105687216 · doi:10.1207/s15516709cog0000_68

Memory and Mystery: The Cultural Selection of Minimally Counterintuitive Narratives

2006· article· en· W2105687216 on OpenAlexafffund
Ara Norenzayan, Scott Atran, Jason Faulkner, Mark Schaller

Bibliographic record

VenueCognitive Science · 2006
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCounterintuitiveNarrativeSelection (genetic algorithm)Cultural memoryPsychologyCognitive scienceComputer scienceCognitive psychologyHistoryLiteratureSociologyArtArtificial intelligenceEpistemologyPhilosophyAnthropology

Abstract

fetched live from OpenAlex

We hypothesize that cultural narratives such as myths and folktales are more likely to achieve cultural stability if they correspond to a minimally counterintuitive (MCI) cognitive template that includes mostly intuitive concepts combined with a minority of counterintuitive ones. Two studies tested this hypothesis, examining whether this template produces a memory advantage, and whether this memory advantage explains the cultural success of folktales. In a controlled laboratory setting, Study 1 found that an MCI template produces a memory advantage after a 1-week delay, relative to entirely intuitive or maximally counterintuitive cognitive templates. Using archival methods, Study 2 examined the cognitive structure of Grimm Brothers folktales. Compared to culturally unsuccessful folktales, those that were demonstrably successful were especially likely to fit an MCI template. These findings highlight the role of human memory processes in cultural evolution.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.301
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations385
Published2006
Admission routes2
Has abstractyes

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