Nature, Nurture, and Nous: The Role of Reflection and Reason in the Nature-Nurture Debate
Bibliographic record
Abstract
Biological and social factors (i.e., nature and nurture) interact to give rise to specific human attributes; but they alone are insufficient to explain our most uniquely human attributes: our creative ideas, artifacts, and cultures, and the diverse worldviews that underlie them. We posit that these attributes stem from the capacity for an autonomous, generative stream of thought; it relies on nous. The word ‘nous’ comes from the Greeks, and refers to intellect, understanding, thought, or reason. Our abstract thoughts, ideas, and unique creative styles, and how they crystallize into distinct ways of seeing, being, and contributing to the world may seem harder to pin down or study scientifically than innate drives (nature) and socially learnt (nurture). We show how these attributes can be modelled using autocatalytic networks. Originally used to model the origin of life, autocatalytic networks provide an abstract formal framework with which to model the emergence and growth of networks; not just the networks of catalytic molecules at the core of biological evolution, but also the cognitive structures at the core of biological evolution. Applied to culture, they allow us to model the cognitive steps involved in how knowledge obtained through social or individual learning (modelled as foodset items) ‘catalyze’ the generation of new ideas and perspectives (foodset derived items), and to track lineages of cultural adaptation and change. We outline how they have bene used to model significant transitions in human cultural prehistory, as well as cognitive development in the mind of a child, and cultural discontinuities caused by cross-domain transfer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.051 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".