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Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning

机译：电子游戏遥测技术是复杂技能学习研究中的关键工具

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Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertise only yields deep insight into development where differences are important throughout skill acquisition. This reliance may be pernicious where the predictive importance of variables is not constant across levels of expertise. Before the development of sophisticated machine learning tools for data mining larger samples, and indeed, before such samples were available, it was difficult to test the implicit assumption of static variable importance in expertise development. To investigate if this reliance may have imposed critical restrictions on the understanding of complex skill development, we adopted an alternative method, the online acquisition of telemetry data from a common daily activity for many: video gaming. Using measures of cognitive-motor, attentional, and perceptual processing extracted from game data from 3360 Real-Time Strategy players at 7 different levels of expertise, we identified 12 variables relevant to expertise. We show that the static variable importance assumption is false - the predictive importance of these variables shifted as the levels of expertise increased - and, at least in our dataset, that a contrastive approach would have been misleading. The finding that variable importance is not static across levels of expertise suggests that large, diverse datasets of sustained cognitive-motor performance are crucial for an understanding of expertise in real-world contexts. We also identify plausible cognitive markers of expertise.
机译：长期以来，认知科学一直表现出对专业知识的兴趣，部分原因是对专家发展的预测和控制将具有巨大的实用价值。该领域的大多数研究通过将专家与新手进行比较来研究专业知识。在人类专业知识研究中依赖对比样本只会对发展产生深刻的洞察力，而差异在整个技能获取过程中都很重要。如果变量的预测重要性在专业知识水平上不是恒定的，那么这种依赖可能是有害的。在开发用于数据挖掘的大型样本的复杂机器学习工具之前，实际上，在此类样本可用之前，很难测试静态变量对专业知识开发的重要性的隐含假设。为了研究这种依赖是否可能对理解复杂技能发展施加了严格限制，我们采用了一种替代方法，即从许多人的日常活动中在线获取遥测数据：视频游戏。使用从7种不同专业水平的3360实时策略玩家的游戏数据中提取的认知运动，注意力和知觉处理的度量，我们确定了12个与专业相关的变量。我们表明，静态变量重要性假设是错误的-这些变量的预测重要性随着专业水平的提高而发生了变化-至少在我们的数据集中，对比方法会产生误导。可变性重要性在专业知识水平上并非一成不变的发现表明，持续认知运动表现的大型，多样化数据集对于理解现实世界中的专业知识至关重要。我们还确定了专业知识的合理认知标记。

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