Humans can identify cats’ affective states from subtle facial expressions
Bibliographic record
Abstract
Abstract Although cats’ popularity as pets rivals that of dogs, cats are little studied, and people's abilities to read this apparently ‘inscrutable’ species have attracted negligible research. To determine whether people can identify feline emotions from cats’ faces, participants (n = 6,329) each viewed 20 video clips of cats in carefully operationalised positively (n =10) or negatively valenced states (n = 10) (cross-factored with low and high activity levels). Obvious cues (eg open mouths or fully retracted ears) were eliminated. Participants’ average scores were low (11.85/20 correct), but overall above chance; furthermore, 13% of participants were individually significantly successful at identifying the valence of cats’ states (scoring ≥ 15/20 correct). Women were more successful at this task than men, and younger participants more successful than older, as were participants with professional feline (eg veterinary) experience. In contrast, personal contact with cats (eg pet-owning) had little effect. Cats in positive states were most likely to be correctly identified, particularly if active rather than inactive. People can thus infer cats’ affective states from subtle aspects of their facial expressions (although most find this challenging); and some individuals are very good at doing so. Understanding where such abilities come from, and precisely how cats’ expressions change with affective state, could potentially help pet owners, animal care staff and veterinarians optimise feline care 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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".