The Evolution of Complementary Cognition: Humans Cooperatively Adapt and Evolve through a System of Collective Cognitive Search
Bibliographic record
Abstract
We propose a new theory of human cognitive evolution, which we term Complementary Cognition. We build on evidence for individual neurocognitive specialization regarding search abilities in the modern population, and propose that our species cooperatively searches and adapts through a system of group-level cognition. This paper sets out a coherent theory to explain why Complementary Cognition evolved and the conditions responsible for its emergence. Using the framework of search, we show that Complementary Cognition can be contextualized as part of a hierarchy of systems including genetic search and cognitive search. We propose that, just as genetic search drives phenotypic adaptation and evolution, complementary cognitive search is central to understanding how our species adapts and evolves through culture. Complementary Cognition has far-reaching implications since it may help to explain the emergence of behavioural modernity and provides a new explanatory framework for why language and many aspects of cooperation evolved. We believe that Complementary Cognition underpins our species’ success and has important implications for how modern-day systems are designed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".