Investigating the capabilities of large vision language models in dog emotion recognition.
Bibliographic record
Abstract
Identifying emotional states in animals is a key challenge in behavioural science and a prerequisite for developing reliable welfare assessments, ethical frameworks, and robust human-animal communication models. Recently, large vision-language models (LVLMs) such as GPT-4o, Gemini, and LLaVA have shown promise in general image understanding tasks, and are beginning to be applied for emotion recognition in animals. In this study, we critically evaluated the ability of state-of-the-art LVLMs to classify emotional states in dogs using a zero-shot approach. We assessed model performance on two datasets: (1) the Dog Emotions (DE) dataset, consisting of web-sourced images with layperson-generated emotion labels, and (2) the Labrador Retriever cropped-face (LRc) dataset, which stems from a rigorously controlled experimental study where emotional states were systematically elicited in dogs and defined based on the experimental context in canine emotion research. Our results revealed that while LVLMs showed moderate classification accuracy on DE, performance is likely driven by superficial correlations, such as background context and breed morphology. When evaluated on LRc, where emotional states are experimentally induced and backgrounds are minimal, performance dropped to near-chance levels, indicating limited ability to generalise based on biologically relevant cues. Background manipulation experiments further confirmed that models relied heavily on contextual features. Prompt variation and system-level instructions slightly improved response rates but did not enhance classification accuracy. These findings highlight significant limitations in the current application of LVLMs to non-human species and raise ethical and epistemological concerns regarding potential anthropocentric biases embedded in their training data. We advocate for species-sensitive AI approaches grounded in validated behavioural science, emphasising the need for high-quality, preferably experimentally-based multimodal datasets and more transparent validation. Our study underscores both the potential and the risks of using general-purpose AI to infer internal states in animals and calls for rigorous, interdisciplinary development of animal-centred computational approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".