Innovating Innovation Rate and Its Relationship with Brains, Ecology and General Intelligence
Bibliographic record
Abstract
the ecology of intelligence, while Wyles et al. [1983] had proposed that innovations, especially when they were socially transmitted, might serve as behavioral drivers of evolution, using the famous example of tits opening milk bottles. Around 1994, I started wondering whether there could be many more cases of innovations besides milk bottle opening hidden in the ornithology literature, and whether these cases could provide a valid quantitative estimate of cognition. The publication of McGill Biology colleague Rob Peters’ influential book The Ecological Implications of Body Size [Peters, 1983] (currently 3,920 citations on Google Scholar) gave me a kind of ‘quantification envy’ that animal cognition could be as ‘operationalizable’ as body size and used in a similar manner in comparative analyses. Initially, the innovation project targeted taxonomic differences in socially acquired versus individually acquired innovations, predicting that, if the taxonomic distribution of the two modes of acquisition did not differ, this would be further support for the argument that social and individual learning are different sides of the same coin. The socially acquired category was soon dropped because too few cases were found in birds, but the data did show for the first time that a field-based quantitative measure of intelligence was positively correlated with relative forebrain size [Lefebvre et al., In 2002, the three of us were working together at McGill University, brought together by our shared interest in animal innovation. We had begun to discuss writing a review on the different aspects of our work on behavioral flexibility, which we felt strengthened and supported one another. An ideal opportunity arose when then editor Walt Wilczynski devoted a special issue of BBE to a symposium on ‘Ecology and the Central Nervous System’, organized by Luc-Alain Giraldeau at the 2002 International Society for Behavioral Ecology congress in Montreal. In the paper we were able to discuss and review a new operational measure of cognition, innovation rate. Using innovation rate and related measures of behavioral flexibility, we provided evidence for convergent cognitive evolution in birds and primates, and for behavioral flexibility having important ecological and evolutionary consequences. Broadly, our contributions can be separated into three themes, and we discuss the genesis of each in turn.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".