Can Ponies (Equus Caballus) Distinguish Human Facial Expressions?
Bibliographic record
Abstract
Communication within a species is essential for access to resources, alerting to dangers, group facilitation and social bonding; human facial expressions are considered to be an important factor in one’s ability to communicate with others. Evidence has shown that dogs and horses are able to distinguish positive and negative facial expressions by observing photographs of humans, however there is currently no research on how facial expressions from a live human are perceived by horses. This study investigated how ponies distinguish facial expressions presented by live actors. Trained actors (n = 2), using the human Facial Action Coding System, displayed four facial expressions (anger, sadness, joy and neutral) individually to twenty ponies. Heart rate and behaviors of the ponies including first monocular eye look, eye look duration (right and left side bias) and latency to approach were observed. A generalized linear mixed model (GLIMMIX) using Sidak’s multiple comparisons of least squared means determined that when exposed to anger expressions ponies looked more often with their left eye first and when exposed to joy, looked more often with their right eye first (p = 0.011). The ponies spent more time looking at angry expressions (p = 0.0003) in comparison to other expressions. There was no variation in heart rate across expressions (p > 0.89). Regardless of human facial expression, ponies looked longer (p = 0.0035), took longer to approach (p = 0.0297) and displayed more oral behaviours (p < 0.0001) with one actor than the other indicating increased arousal or negative valence. Ponies with more experience as a lesson mount had lower heart rates (p < 0.0001) carried their head lower (p < 0.0001), kept their left ear on the actor (p < 0.03) and exhibited more oral behaviours (p < 0.0001) than ponies with less experience. This study demonstrates that ponies are able to distinguish facial expressions presented by a live human, but other factors also contribute to their responses to humans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".