An Agent-based Model of the Cognitive Mechanisms Underlying the Origins\n of Creative Cultural Evolution
Bibliographic record
Abstract
Human culture is uniquely cumulative and open-ended. Using a computational\nmodel of cultural evolution in which neural network based agents evolve ideas\nfor actions through invention and imitation, we tested the hypothesis that this\nis due to the capacity for recursive recall. We compared runs in which agents\nwere limited to single-step actions to runs in which they used recursive recall\nto chain simple actions into complex ones. Chaining resulted in higher cultural\ndiversity, open-ended generation of novelty, and no ceiling on the mean fitness\nof actions. Both chaining and no-chaining runs exhibited convergence on optimal\nactions, but without chaining this set was static while with chaining it was\never-changing. Chaining increased the ability to capitalize on the capacity for\nlearning. These findings show that the recursive recall hypothesis provides a\ncomputationally plausible explanation of why humans alone have evolved the\ncultural means to transform this planet.\n
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".