Facial expression in humans as a measure of empathy towards farm animals in pain
Bibliographic record
Abstract
People often express concern for the welfare of farm animals, but research on this topic has relied upon self-report. Facial expressions provide a quantifiable measure of emotional response that may be less susceptible to social desirability bias and other issues associated with self-report. Viewing other humans in pain elicits facial expressions indicative of empathy. Here we provide the first evidence that this measure can also be used to assess human empathetic responses towards farm animals, showing that facial expressions respond reliably when participants view videos of farm animals undergoing painful procedures. Participants (n = 30) were asked to watch publicly sourced video clips of cows and pigs undergoing common management procedures (e.g. disbudding, castration, tail docking) and control videos (e.g. being lightly restrained, standing). Participants provided their subjective rating of the intensity of 5 negative emotions (pain, sadness, anger, fear, disgust) on an 11-point Likert scale. Videos of the participants (watching the animals) were scored for intensity of unpleasantness of the participants' facial expression (also on an 11-point Likert scale) by a trained observer who was blind to treatment. Participants showed more intense facial expressions while viewing painful procedures versus control procedures (mean ± SE Likert; 2.4 ± 0.08 versus 0.6 ± 0.17). Participants who reported more intense negative responses also showed stronger facial expressions (slope ± SE = 0.4 ± 0.04). Both the self-reported and facial measures varied with species and procedure witnessed. These results indicate that facial expressions can be used to assess human-animal empathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".