Can Sol's Explanation for the Evolution of Animal Innovation Account for Human Innovation?
Bibliographic record
Abstract
Sol argues that innovation propensity is not a specialized adaptation resulting from targeted selection but an instance of exaptation because selection cannot act on situations that are only encountered once. In exaptation, a trait that originally evolved to solve one problem is co-opted to solve a new problem; thus the trait or traits in question must be necessary and sufficient to solve the new problem. Sol claims that traits such as persistence and neophilia, are necessary and sufficient for animal innovation, which is a matter of trial and error. We suggest that this explanation does not extend to human innovation, which involves strategy, logic, intuition, and insight, and requires traits that evolved, not as a byproduct of some other function, but for the purpose of coming up with adaptive responses to environmental variability itself. We point to an agent based model that indicates the feasibility of two such proposed traits: (1) chaining, the ability to construct complex thoughts from simple ones, and (2) contextual focus, the ability to shift between convergent and divergent modes of thought. We agree that there is a sense in which innovation is exaptation--it occurs when an existing object or behaviour is adapted to new needs or tastes--and refer to a mathematical model of biological and cultural exaltation. We conclude that much is gained by comparing and contrasting animal and human innovation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".