A fear-directed approach to a novel affective virtual reality game system

情感计算方法在提供引人入胜和身临其境的游戏体验以及促进与情感相关的有目的游戏的效果方面具有潜力。然而，当涉及到对生物反馈数据的评估以及将其解释为情绪状态模型时，存在不一致的发现。这在一定程度上是由于对多个游戏系统的方法不集中，测量设备之间缺乏知情的比较，以及对情绪的普遍看法，降低了情感数据的复杂性和有用性。因此，这项拟议的研究将只关注VR系统和与恐惧相关的体验，以建立一个系统，以最适合VR游戏的可能最佳方式准确而可靠地输出情感数据。继而，该系统将能够促进与情绪相关的游戏发展，移情社会主体以实现更好的互动，并为利用视频游戏中的情感数据进行有效的心理训练干预提供新的证据。

Affective computing methods have presented potential in providing engaging and immersive game experiences, as well as in facilitating the effects of purposeful games related to emotions. There are however inconsistent findings when it comes to the assessment of biofeedback data and its interpretation into a model of emotional states. This is partly due to unfocused approaches over multiple gaming systems, a lack of informed comparisons between measurement devices, and a generalized view of emotions, reducing the complexity and usefulness of affective data. The proposed study will therefore focus only on VR systems and fear-related experiences to build a system that precisely and reliably outputs emotional data in the best possible way suited for VR games. Upon succession, this system will be able to advance emotion-related gameplay development, empathetic social agents for better interactions, as well as provide new evidence for effective psychological training interventions using affective data in video games.