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Video game play, attention, and learning: How to shape the development of attention and influence learning?

机译:电子游戏的玩法,注意力和学习:如何塑造注意力的发展并影响学习?

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Purpose of review: The notion that play may facilitate learning has long been touted. Here, we review how video game play may be leveraged for enhancing attentional control, allowing greater cognitive flexibility and learning and in turn new routes to better address developmental disorders. Recent findings: Video games, initially developed for entertainment, appear to enhance the behavior in domains as varied as perception, attention, task switching, or mental rotation. This surprisingly wide transfer may be mediated by enhanced attentional control, allowing increased signal-to-noise ratio and thus more informed decisions. Summary: The possibility of enhancing attentional control through targeted interventions, be it computerized training or self-regulation techniques, is now well established. Embedding such training in video game play is appealing, given the astounding amount of time spent by children and adults worldwide with this media. It holds the promise of increasing compliance in patients and motivation in school children, and of enhancing the use of positive impact games. Yet for all the promises, existing research indicates that not all games are created equal: a better understanding of the game play elements that foster attention and learning as well as of the strategies developed by the players is needed. Computational models from machine learning or developmental robotics provide a rich theoretical framework to develop this work further and address its impact on developmental disorders.
机译:复习的目的:长期以来一直吹捧游戏可能促进学习的观念。在这里,我们回顾了如何利用视频游戏来增强注意力控制,实现更大的认知灵活性和学习能力,进而开辟新途径来更好地解决发育障碍。最新发现:电子游戏最初是为娱乐而开发的,似乎可以增强人们在感知,注意力,任务切换或精神旋转等领域的行为。令人惊讶的广泛转移可能是由增强的注意力控制所介导的,从而提高了信噪比,从而使决策更加明智。简介:通过计算机辅助训练或自我调节技术来进行有针对性的干预以增强注意力控制的可能性现已广为人知。考虑到全世界的儿童和成年人在这种媒体上花费的时间惊人,将这种培训嵌入视频游戏中很有吸引力。它有望增加患者的依从性和学龄儿童的积极性,并增强对正面影响游戏的使用。然而,对于所有的诺言,现有的研究表明并非所有游戏都是平等的:需要更好地理解能引起注意和学习的游戏元素以及玩家制定的策略。来自机器学习或发展机器人技术的计算模型为进一步开展这项工作并解决其对发展障碍的影响提供了丰富的理论框架。

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