Bibliographic record
Abstract
This chapter synthesizes a program of research devoted to developing a scientific framework for understanding human cultural evolution using studies with human participants, computational models, and mathematical analyses. The research program focuses on a set of questions that are interrelated in that each step toward a solution to one of these questions provides fragmented glimpses of solutions to the others. The questions are: 1.How did the capacity for creative culture evolve? Although cultural transmission (in which one individual acquires elements of culture from another) is observed in many species, cultural evolution (in which elements of culture are not just transmitted but adaptively modified) is much rarer, and perhaps unique to our species. Research in psychology, neuroscience, anthropology, archaeology, and genetics, as well as mathematical and computational modelling, is increasingly converging toward a consistent picture of the cognitive and concomitant neural changes over the last few million years that enabled humans to not just understand their world, but transform it.2.What fuels cultural innovation? While the first branch of the research program focuses on historical, biologically evolved changes in the cognitive abilities underlying cultural evolution, this branch is focused on understanding in a precise, rigorous way the mechanisms underlying the creative processes by which cultural novelty is generated. In other words, it aims to understand the process by which existing knowledge and experience comes together to generate new technologies, songs, artworks, scientific theories, and so forth. 3.How does human culture evolve? A third branch of the research program seeks to understand how cultural evolution works, i.e., does culture evolve, as do biological organisms, through natural selection, or by some other means? Are biological and cultural evolution isomorphic, i.e., despite their superficial differences do they share a common algorithmic structure? By comparing and contrasting these two evolutionary processes, we gain a richer understanding of how evolutionary processes get started, and how they work.The research program synthesizes research on cultural evolution, evolutionary theory, and creativity, using tools such as agent-based models and autocatalytic networks. What unites these projects is the aim of developing a psychologically, neurologically, and evolutionarily plausible framework for how ideas unfold over time; not just in the minds of individuals, but through direct and indirect interactions amongst individuals. This interdisciplinary undertaking has the potential to help us understand where we came from, who we are, and where we may be headed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".