Pain and Pessimism: Dairy Calves Exhibit Negative Judgement Bias following Hot-Iron Disbudding
Bibliographic record
Abstract
Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, but emotional states are difficult to directly assess in animals. Researchers have assessed pain using behavioural and physiological measures, but these approaches are limited to understanding the arousal rather than valence of the emotional experience. Cognitive bias tasks show that depressed humans judge ambiguous events negatively and this technique has been applied to assess emotional states in animals. However, limited research has examined how pain states affect cognitive processes in animals. Here we present the first evidence of cognitive bias in response to pain in any non-human species. In two experiments, dairy calves (n = 17) were trained to respond differentially to red and white video screens and then tested with unreinforced ambiguous colours in two or three test sessions before and two sessions after the routine practice of hot-iron disbudding. After disbudding calves were more likely to judge ambiguous colours as negative. This 'pessimistic' bias indicates that post-operative pain following hot-iron disbudding results in a negative change in emotional state.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".