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Enhancing video game performance through an individualized biocybernetic system

机译:通过个性化生物网络系统提高视频游戏性能

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摘要

Biocybernetic systems are physiological software systems that explicitly utilize physiological signals to control or adapt software functionality (Pope et al., 1995.) These systems have tremendous potential for innovation in human computer interaction by using physiological signals to infer a user\u27s emotional and mental states (Allanson \u26 Fairclough, 2004; Fairclough, 2008). Nevertheless, development of these systems has been ultimately hindered by two fundamental challenges. First, these systems make generalizations about physiological indicators of cognitive states across populations when, in fact, relationships between physiological responses and cognitive states are specific to each individual (Andreassi, 2006). Second, they often employ largely inconsistent retrospective techniques to subjectively infer user\u27s mental state (Fairclough, 2008).An individualized biocybernetic system was developed to address the fundamental challenges of biocybernetic research. This system was used to adapt video game difficulty through real-time classifications of physiological responses to subjective appraisals. A study was conducted to determine the system\u27s ability to improve player\u27s performance. The results provide evidence of significant task performance increase and higher attained task difficulty when players interacted with the game using the system than without. This work offers researchers with an alternative method for software adaptation by conforming to the individual characteristics of each user.
机译:生物网络系统是生理软件系统,它明确利用生理信号来控制或调整软件功能(Pope等人,1995年)。这些系统通过使用生理信号来推断用户的情感和心理,在人机交互方面具有巨大的创新潜力。 (Allanson \ u26 Fairclough,2004; Fairclough,2008)。然而,这些系统的开发最终受到两个基本挑战的阻碍。首先,当生理反应和认知状态之间的关系特定于每个人时,这些系统对整个人群的认知状态的生理指标进行了概括(Andreassi,2006)。其次,他们通常采用不一致的回顾性技术来主观地推断用户的心理状态(Fairclough,2008年)。开发了个性化的生物cybernetic系统来应对生物cybernetic研究的基本挑战。该系统用于通过对主观评估的生理反应进行实时分类来适应视频游戏的难度。进行了一项研究,以确定系统改善播放器性能的能力。结果提供了当玩家使用该系统与游戏进行交互时,任务性能显着提高且获得的任务难度更高的证据。这项工作通过符合每个用户的个人特征,为研究人员提供了另一种软件适配方法。

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    Bohner, Ross;

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  • 年度 2010
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  • 正文语种 en
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