Exploring the evolution and ontogeny of imitation and abstraction : a hierarchical Bayesian modelling approach
Bibliographic record
Abstract
Humans have an immense behavioural and cognitive repertoire that has been shaped by cumulative cultural evolution. In this thesis I investigate two cognitive abilities that crucially enlarge the efficiency of skill and knowledge acquisition: 1) the capability for abstraction that enables powerful generalization of information to make wide ranging predictions in new situations and 2) the ability to imitate others which allows the quick and low-risk adoption of new behavioural strategies. Despite decades of accumulating data in both domains, it is still debated to what extent other species share these abilities and how they develop in humans. Solving these persisting disagreements requires an alteration of how data are generated and analysed. The use of computational modelling is a promising way to specify hypotheses, perform more detailed analyses of the underlying cognitive mechanisms and thus ultimately achieve a richer species comparison. In this thesis, I introduce the approach of hierarchical Bayesian modelling to the field of comparative psychology to investigate abstract rule formation and action copying in capuchin monkeys, chimpanzees (only abstract rule formation) and children. In chapters two and three, I outline two studies in which participants had to use sampled evidence to infer abstract rules about the item distributions in containers and efficiently guide behaviour in novel test situations. In chapter four, we investigated how children and capuchin monkeys integrate causal and social information when copying a goal- directed behaviour. Whereas children’s performance was mostly in line with the predictions of the computational models, showing that they are capable of abstraction and consider causal information when imitating, capuchin monkeys performed in all experiments at chance and chimpanzees showed some understanding of abstract rules. In chapter five, I discuss how the modelling approach improved my research and outline the advantages and caveats of its application in future comparative and developmental research.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".