Dog–Human Play, but Not Resting Post-Learning Improve Re-Training Performance up to One Year after Initial Task Acquisition in Labrador Retriever Dogs: A Follow-On Study
Bibliographic record
Abstract
Arousing and emotional situations can improve cognitive performance and the memorability of events. Recently, the enhancement of training performance in Labrador Retriever dogs through 30 min of dog–human play immediately after acquiring a novel task, when compared to a resting period, was demonstrated. This follow-on study used the same pseudo-randomized, counterbalanced, between-subject study design, and 11 Labrador Retrievers were re-trained in the identical two-choice discrimination paradigm after a period of 1 year. The playful activities group needed significantly less trials and made significantly less errors to successfully reach the re-training criterion (Mann–Whitney U test, critical value of U at p < 0.05 is 5, U = 5, Z = 1.73, p = 0.04 and U = 4.5, Z = 1.8, p = 0.03, respectively). Following model simplification of a multiple factor/covariate general linear model analysis, the type of intervention, the number of trials needed to re-learn the task after 24 h, the average heart rate during the intervention a year ago, and age were significantly correlated to the number of trials and errors needed to resolve the task. A significant difference due to intervention allocation (heart rate during the intervention, trials needed to re-learn the task after 24 h) between the groups was confirmed. Age did not significantly differ between the groups; nevertheless, the effects of ageing cannot be fully excluded, given the low sample size. No effects of the trainer and of the cortisol concentrations (of the previous year) were observed. This is the first evidence that post-training activity may influence memory up to 1 year after task acquisition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".