Replication Data for: Stability in Cognitive and Behavioural Performance Varies Between Dog Breed Clades
Bibliographic record
Abstract
The existence and stability of breed-specific cognitive and behavioural profiles remain a topic of ongoing debate. It is not yet clear whether the observed behavioural differences between dog breeds persist consistently over time, and whether the ability to maintain performance on a previously learned behaviour potentially confers differential advantages across breed clades. Using a structured hand-touch learning task, discrimination and reversal learning performance, along with perseverance, and emotionality were assessed in 105 dogs from four breed clades across two testing occasions, while controlling for reward responsiveness, demographic factors, and relevant training history. Perseverance appeared to be a relatively stable trait, whereas discrimination and reversal learning performance seemed to improve across time. The extent of change in performance varied across breed clades, suggesting that breed-specific cognitive profiles may shape behavioural and learning dynamics over time. Initial differences across breed clades in the reversal learning performance were not maintained upon re-exposure to the task, likely due to increased individual variability. In contrast, differences across breed clades in the discrimination learning performance emerged only in the second testing occasion. Thus, depending on the cognitive capacity being assessed, repeated exposure may either obscure or facilitate the expression of underlying breed-specific performance patterns. Furthermore, although the average emotionality decreased, emotionality changes did not consistently align with breed clades’ performance improvement, which may suggest a potential dissociation between cognitive performance and emotional reactivity in some breed clades. Where each breed falls within the cognitive stability-flexibility continuum requires further investigation.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.040 | 0.041 |
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".