Animal and human innovation: novel problems and novel solutions
Bibliographic record
Abstract
This theme issue explores how and why behavioural innovation occurs, and the consequences of innovation for individuals, groups and populations. A vast literature on human innovation exists, from the development of problem-solving in children, to the evolution of technology, to the cultural systems supporting innovation. A more recent development is a growing literature on animal innovation, which has demonstrated links between innovation and personality traits, cognitive traits, neural measures, changing conditions, and the current state of the social and physical environment. Here, we introduce these fields, define key terms and discuss the potential for fruitful exchange between the diverse fields researching innovation. Comparisons of innovation between human and non-human animals provide opportunities, but also pitfalls. We also summarize some key findings specifying the circumstances in which innovation occurs, discussing factors such as the intrinsic nature of innovative individuals and the environmental and socio-ecological conditions that promote innovation, such as necessity, opportunity and free resources. We also highlight key controversies, including the relationship between innovation and intelligence, and the notion of innovativeness as an individual-level trait. Finally, we discuss current research methods and suggest some novel approaches that could fruitfully be deployed.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.010 | 0.032 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".