迈向智能用户界面:预测电脑游戏中的动作

Hendrik Koesling, A. Kenny, A. Finke, H. Ritter, S. McLoone, Tomas E. Ward
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引用次数: 5

摘要

该研究展示了第一人称射击游戏(FPS)中眼球运动的在线处理如何帮助预测玩家关于后续行动的决策。基于行动控制理论,我们确定了不同的认知取向在决策前和决策后阶段。认知取向在注意广度或“接受度”方面有所不同:在决策前阶段,玩家处理尽可能多的信息,然后在决策后阶段专注于实施预期的行动。参与者观看FPS游戏的动画序列,并决定要拯救哪个游戏角色以及如何执行他们的行动。眼球运动数据显示,在决策前阶段和决策后阶段,注意广度之间存在明显区别,这支持了行动阶段的卢比孔河模型。当目标意向形成时,注意力迅速缩小。我们发现在目标形成(“认知卢比孔河”)和运动反应之间有800- 900毫秒的滞后。游戏引擎可能会使用这种延迟来预测玩家尚未执行的动作。因此,带有注视依赖、注视控制预期模块的用户界面应该增强游戏角色的行为,让他们变得更“聪明”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards intelligent user interfaces: anticipating actions in computer games
The study demonstrates how the on-line processing of eye movements in First Person Shooter (FPS) games helps to predict player decisions regarding subsequent actions. Based on action-control theory, we identify distinct cognitive orientations in pre- and post-decisional phases. Cognitive orientations differ with regard to the width of attention or "re-ceptiveness": In the pre-decisional phase players process as much information as possible and then focus on implementing intended actions in the post-decisional phase. Participants viewed animated sequences of FPS games and decided which game character to rescue and how to implement their action. Oculomotor data shows a clear distinction between the width of attention in pre- and post-decisional phases, supporting the Rubicon model of action phases. Attention rapidly narrows when the goal intention is formed. We identify a lag of 800--900 ms between goal formation ("cognitive Rubicon") and motor response. Game engines may use this lag to anticipatively respond to actions that players have not executed yet. User interfaces with a gaze-dependent, gaze-controlled anticipation module should thus enhance game character behaviours and make them much "smarter".
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