视觉语言模型是否能理解游戏中的玩家参与度？
Do Vision Language Models Understand Human Engagement in Games?
AI总结：
研究评估了三种视觉语言模型在不同提示策略下的表现，发现零样本预测效果差，而基于理论的提示策略难以有效提升参与度预测能力。
摘要：
从游戏录像中推断玩家参与度对游戏设计和玩家体验研究至关重要，但尚不清楚视觉语言模型（VLMs）能否仅凭视觉线索推断这种潜在心理状态。使用包含九款第一人称射击游戏的GameVibe少样本数据集，我们评估了三种VLMs在六种提示策略下的表现，包括零样本预测、基于Flow、GameFlow、自我决定理论和MDA的理论引导提示，以及检索增强提示。我们考虑了单点参与度预测和连续窗口间参与度变化的成对预测。结果表明，零样本VLM预测通常较弱，往往无法超越简单的每款游戏多数类基线。基于记忆或检索的增强提示在某些设置中提高了单点预测，而成对预测在所有策略中都保持困难。基于理论的提示单独使用并不能可靠提升性能，反而可能强化表面层面的捷径。这些发现表明当前VLMs存在感知与理解之间的差距：尽管它们可以识别可见的游戏玩法线索，但仍然难以在不同游戏中稳健地推断玩家参与度。
英文摘要：
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 generally weak and often fail to outperform simple per-game majority-class baselines. Memory- or retrieval-augmented prompting improves pointwise prediction in some settings, whereas pairwise prediction remains consistently difficult across strategies. Theory-guided prompting alone does not reliably help and can instead reinforce surface-level shortcuts. These findings suggest a perception--understanding gap in current VLMs: although they can recognize visible gameplay cues, they still struggle to robustly infer human engagement across games.
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