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Record W1870735863 · doi:10.48550/arxiv.1310.4086

A Computational Model of Two Cognitive Transitions Underlying Cultural Evolution

2013· preprint· en· W1870735863 on OpenAlexaff
Liane Gabora, Wei Wen Chia, Hadi Firouzi

Bibliographic record

VenuearXiv (Cornell University) · 2013
Typepreprint
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChainingSurvival of the fittestDiversity (politics)Sociocultural evolutionFitness functionCognitionMode (computer interface)Focus (optics)Fitness landscapeFunction (biology)PsychologyCognitive scienceCognitive psychologyComputer scienceSociologyEpistemologyEvolutionary biologyBiologyDevelopmental psychologyPhilosophyMachine learningGenetic algorithmHuman–computer interactionNeuroscience

Abstract

fetched live from OpenAlex

We tested the computational feasibility of the proposal that open-ended cultural evolution was made possible by two cognitive transitions: (1) onset of the capacity to chain thoughts together, followed by (2) onset of contextual focus (CF): the capacity to shift between a divergent mode of thought conducive to 'breaking out of a rut' and a convergent mode of thought conducive to minor modifications. These transitions were simulated in EVOC, an agent-based model of cultural evolution, in which the fitness of agents' actions increases as agents invent ideas for new actions, and imitate the fittest of their neighbors' actions. Both mean fitness and diversity of actions across the society increased with chaining, and even more so with CF, as hypothesized. CF was only effective when the fitness function changed, which supports its hypothesized role in generating and refining ideas.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.160
GPT teacher head0.268
Teacher spread0.108 · 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 designSimulation or modeling
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

Citations10
Published2013
Admission routes1
Has abstractyes

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