Assessing the motivation to learn in cattle
Bibliographic record
Abstract
Cognitive challenges may provide a form of enrichment to improve the welfare of captive animals. Primates, dolphins, and goats will voluntarily participate in learning tasks suggesting that these are rewarding, but little work has been conducted on livestock species. We investigated the motivation of 10 pairs of Holstein heifers to experience learning opportunities using a yoked design. All heifers were trained to perform an operant response (nose touch) on a variable interval schedule. Learning heifers then performed this response to access a discrimination learning task in which colour and texture of feed-bin lids signified a preferred reward (grain) vs. a non-preferred reward (straw). Control heifers received the same feed without a choice of bins or association of feed with lid type. Learning heifers approached the target to begin sessions faster (p = 0.024) and tended to perform more operant responses (p = 0.08), indicating stronger motivation. Treatments did not differ in the frequency with which heifers participated in voluntary training sessions. We conclude that heifers are motivated to participate in learning tasks, but that aspects of the experience other than discrimination learning were also rewarding. Cognitive challenges and other opportunities to exert control over the environment may improve animal welfare.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".