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Towards biofeedback-controlled self-rewarding learning with mobile devices

机译：通过移动设备向生物融合控制的自我奖励学习

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Over the past decades, reinforcement learning has been applied in several fields of computer science (especially in machine learning) as a learning algorithm. The same model can be used in cognitive neuroscience. In this case, neurotransmitters are providing the rewarding signal, and therefore in the background a similar learning process occurs as in reinforcement learning. According to previous studies, the same chemicals are produced through playing video games. In this paper, we aim to show a few methods and promising approaches to advance a biofeedback-controlled self-rewarding learning framework, which can be established in numerous applications. In recent times, mobile devices (phones and tablets as well) acquire more and more popularity among teenagers. Our objective is to develop a framework on these devices to measure and interpret neural activity and learning progress in general, so a related system can be developed to sustain attention and as a result, adaptive computer games may be developed in the near future.
机译：在过去的几十年中，加强学习已应用于几个计算机科学领域（特别是在机器学习中）作为学习算法。相同的型号可用于认知神经科学。在这种情况下，神经递质正在提供奖励信号，因此在背景中，在加强学习中发生类似的学习过程。根据以前的研究，通过播放视频游戏产生相同的化学品。在本文中，我们的目标是展示一些方法和有希望的方法来推进生物融合控制的自我奖励学习框架，这可以在许多应用中建立。最近，移动设备（手机和平板电脑）在青少年中获得越来越多的人气。我们的目标是在这些设备上制定一个框架，以衡量和解释神经活动和学习进度一般，因此可以开发相关系统以获得关注，因此，可以在不久的将来开发自适应电脑游戏。

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