Sensitivity to reward loss as an indicator of animal emotion and welfare
Bibliographic record
Abstract
The scientific study of animal emotion is an important emerging discipline in subjects ranging from neuroscience to animal welfare research. In the absence of direct measures of conscious emotion, indirect behavioural and physiological measures are used. However, these may have significant limitations (e.g. indicating emotional arousal but not valence (positivity versus negativity)). A new approach, taking its impetus from human studies, proposes that biases in information processing, and underlying mechanisms relating to the evaluation of reward gains and losses, may reliably reflect emotional valence in animals. In general, people are more sensitive to reward losses than gains, but people in a negative affective state (e.g. depression) are particularly sensitive to losses. This may underlie broader findings such as an enhanced attention to, and memory of, negative events in depressed individuals. Here we show that rats in unenriched housing, who typically exhibit indicators of poorer welfare and a more negative affective state than those in enriched housing, display a prolonged response to a decrease in anticipated food reward, indicating enhanced sensitivity to reward loss. Sensitivity to reward reduction may thus be a valuable new indicator of animal emotion and 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".