Do Vision Language Models Understand Human Engagement in Games?
Inferring human engagement from gameplay video is important for game design and player-experience research, yet it remains unclear whether vision--language models (VLMs) can infer such latent psychological states from visual cues alone. Using the GameVibe Few-Shot dataset across nine first-person shooter games, we evaluate three VLMs under six prompting strategies, including zero-shot prediction, theory-guided prompts grounded in Flow, GameFlow, Self-Determination Theory, and MDA, and retrieval-augmented prompting. We consider both pointwise engagement prediction and pairwise prediction of engagement change between consecutive windows. Results show that zero-shot VLM predictions are gener...
| Authors | Ziyi Wang, Qizan Guo, Rishitosh Singh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 1 October 2026 |
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