The life-history basis of behavioural innovations
Bibliographic record
Abstract
The evolutionary origin of innovativeness remains puzzling because innovating means responding to novel or unusual problems and hence is unlikely to be selected by itself. A plausible alternative is considering innovativeness as a co-opted product of traits that have evolved for other functions yet together predispose individuals to solve problems by adopting novel behaviours. However, this raises the question of why these adaptations should evolve together in an animal. Here, we develop the argument that the adaptations enabling animals to innovate evolve together because they are jointly part of a life-history strategy for coping with environmental changes. In support of this claim, we present comparative evidence showing that in birds, (i) innovative propensity is linked to life histories that prioritize future over current reproduction, (ii) the link is in part explained by differences in brain size, and (iii) innovative propensity and life-history traits may evolve together in generalist species that frequently expose themselves to novel or unusual conditions. Combined with previous evidence, these findings suggest that innovativeness is not a specialized adaptation but more likely part of a broader general adaptive system to cope with changes in the environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".