Modeling a Cognitive Transition at the Origin of Cultural Evolution\n using Autocatalytic Networks
Bibliographic record
Abstract
Autocatalytic networks have been used to model the emergence of\nself-organizing structure capable of sustaining life and undergoing biological\nevolution. Here, we model the emergence of cognitive structure capable of\nundergoing cultural evolution. Mental representations of knowledge and\nexperiences play the role of catalytic molecules, and interactions amongst them\n(e.g., the forging of new associations) play the role of reactions, and result\nin representational redescription. The approach tags mental representations\nwith their source, i.e., whether they were acquired through social learning,\nindividual learning (of pre-existing information), or creative thought\n(resulting in the generation of new information). This makes it possible to\nmodel how cognitive structure emerges, and to trace lineages of cumulative\nculture step by step. We develop a formal representation of the cultural\ntransition from Oldowan to Acheulean tool technology using Reflexively\nAutocatalytifc and Food set generated (RAF) networks. Unlike more primitive\nOldowan stone tools, the Acheulean hand axe required not only the capacity to\nenvision and bring into being something that did not yet exist, but\nhierarchically structured thought and action, and the generation of new mental\nrepresentations: the concepts EDGING, THINNING, SHAPING, and a meta-concept,\nHAND AXE. We show how this constituted a key transition towards the emergence\nof semantic networks that were self-organizing, self-sustaining, and\nautocatalytic, and discuss how such networks replicated through social\ninteraction. The model provides a promising approach to unraveling one of the\ngreatest anthropological mysteries: that of why development of the Acheulean\nhand axe was followed by over a million years of cultural stasis.\n
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".