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首页> 外文期刊>Topics in cognitive science >Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real-Time, Dynamic Decision-Making Task
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Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real-Time, Dynamic Decision-Making Task

机译：询问特征学习模型以发现实时，动态决策任务中人类专业知识发展的见解

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Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, (a) choosing the goal or objective function that will maximize performance and (b)a feature-based analysis of the current game board to determine where to place the currently falling zoid (i.e., Tetris piece) so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning (CERL) models (Szita & Lorincz, 2006) to determine whether different goals result in different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged (i.e., variable) heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems.
机译：俄罗斯方块提供了一个困难，动态的任务环境，其中一些人是新手，而其他人经过多年的工作和实践成为了极端的专家。在这里，我们学习两个核心技能；即，（a）选择将使性能最大化的目标或目标功能，以及（b）对当前游戏板的基于特征的分析，以确定将当前下降的Zoid（即俄罗斯方块）放置在何处，从而最大程度地实现目标。在研究1中，我们建立了交叉熵强化学习（CERL）模型（Szita和Lorincz，2006），以确定不同的目标是否导致不同的特征权重。这些优化策略中的两个快速上升到性能平稳期，而另两个优化策略继续朝着更高但参差不齐的（即可变的）高度发展。在研究2中，我们将最佳CERL模型做出的Zoid放置决定与67个人的决定进行了比较。在370131次人类游戏情节中，两个CERL模型选择了相同的Zoid布局，分别是我们得分最低的人（占43％的位置）和我们三个得分最高的专家（占65％的位置）。我们的发现表明，人们专注于最大化点，而不是清除的行数或达到的级别数。他们还表明，目标选择会影响CERL的Zoid位置选择，并表明人类也是如此。俄罗斯方块具有重复的任务结构，与许多更复杂的游戏或任务相比，俄罗斯方块更易于处理，更像传统的实验心理学范例。因此，尽管俄罗斯方块很复杂，但并没有压倒性的复杂，它对认知理论，特别是综合认知系统的认知理论提出了正确的挑战。

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