Studying microevolutionary processes in cognitive traits: a comment on Rowe and Healy
Bibliographic record
Abstract
A recent invited review aims to stimulate research on the evolutionary significance of variation in cognitive ability within natural populations (Rowe and Healy 2014). Instead we fear progress in this new interdisciplinary approach may be inadvertently stifled because the reader is left with the impression that useful progress is near impossible—a view with which we disagree. Rowe and Healy list 3 sets of problems that we address in turn. Rowe and Healy (2014) argue further that selection should not necessarily act on “measures of cognition” but that you would expect to see animals that have a range of cognitive abilities optimized for their environment. Often, if not usually, traits are not free to evolve independently of one another and cannot necessarily be optimized by selection—for example, dopamine seems integral to the reward process in learning but is also linked to personality (Frank and Fossella 2011), meaning multiple functional traits will be linked via genetic correlation. The point we want to put across is that exploring which of these microevolutionary processes is likely to be taking place is fascinating and well worth studying.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.024 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.057 | 0.099 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".