Replication Data for: Breed and Individual Variation in Win–Stay/Lose–Shift Behaviour and Perseverance for Food Rewards in Domestic Dogs (Canis familiaris)
Bibliographic record
Abstract
Food-driven behaviours reflect how animals balance costs and benefits of obtaining food, a process shaped by underlying motivation and previous learning experiences. In domestic dogs, centuries of artificial selection resulted in marked variations in motivational and cognitive profiles across breeds and individuals. Yet, how these differences jointly influence behaviours in food-related contexts remains poorly understood. Labrador Retrievers (n = 24) and German Shepherd Dogs (n = 24) completed (a) an object-choice task, assessing outcome-based behavioural strategies (win-stay/lose-shift), and (b) a hand-touch progressive-ratio (PR) task, measuring perseverance and willingness to work for food. Statistical models compared breeds and individuals while accounting for questionnair e-based measures of food motivation, impulsivity, frustration, and training history. Labrador Retrievers reached higher PR breakpoints than German Shepherd Dogs, showing greater willingness to work for food. Higher breakpoints were also observed in dogs with advanced training experiences. Furthermore, dogs were less likely to shift after wins than after losses , suggesting that they adjusted their behaviour based on the outcome of their most recent choice. However, frustration tolerance modulated this pattern from typical win-stay/lose-shift responding. Labrador Retrievers shifted more often than German Shepherd Dogs. However, Labradors’ shifting propensity was markedly modulated by their individual valuation of food, as dogs with higher food motivation scores favoured staying on previously rewarded choices. Furthermore, advanced training was associated with choice perseverance, reflected in higher win–stay, reduced lose–shift, and an increased likelihood of developing a side bias. Collectively, findings highlight both breed-specific and individual differences in food-driven behaviours of dogs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".