Replication Data for: Fundamental Learning Differences Across Five Dog Breed Clades: Breed-specific Predispositions, Individual Experiences, or a Fusion of Both?
Bibliographic record
Abstract
Learning is crucial for shaping domestic dogs’ behaviour through life experiences, yet not all breeds exhibit the same learning aptitude towards a particular task. The current study's objective was to identify differences in fundamental learning features across and within five breed clades and elucidate the underlying factors contributing into these differences. Dogs (N=111) from five different breed clades (UK Rural, Retrievers, Asian Spitz, European Mastiff, and New World) participated in a virtual learning task with their owners. Owners completed validated questionnaires of Impulsivity and Reward Responsiveness. The learning task comprised of reinforcing an arbitrary behaviour (hand-touch) through multiple sessions of Acquisition (reinforcing the hand-touch), Discrimination (reinforcing the hand-touch on one of two hands) and Reversal Learning (reinforcing the hand-touch on the opposite hand), followed by a single session of Extinction (hand-touch not reinforced). Results showed notable differences across the studied breed clades in certain learning components. However, these disparities may not solely stem from inherent cognitive variations among breeds but rather potentially influenced by contextual factors such as the human-dog communication dynamics attributed to breeds’ cooperativity. Furthermore, breed clades differed in the contributing factors predicting individual learning performances, which also highlights the potential effect of breeds’ historical function.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.023 |
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".