2014年5月28日水曜日

Spatial Generalization in Operant Learning: Lessons from Professional Basketball

Tal Neiman, Yonatan Loewenstein
PLOS Computational Biology
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003623

オペラント学習における「般化(過去の学習結果を似たような状況に応用する)」の研究。
NBA選手のデータで検証。「成功したので、同じような位置からシュートをしよう」という「地理的な般化」ではなく、「3ptが成功したので、また3ptシュートを打とう」という「ゲームのルールに基づく高度な般化」が見られた。
つまり、般化は(抽象的なゲームのルールの理解のような)高度な認知処理の結果を反映できる。

In operant learning, behaviors are reinforced or inhibited in response to the consequences of similar actions taken in the past. However, because in natural environments the “same” situation never recurs, it is essential for the learner to decide what “similar” is so that he can generalize from experience in one state of the world to future actions in different states of the world. The computational principles underlying this generalization are poorly understood, in particular because natural environments are typically too complex to study quantitatively. In this paper we study the principles underlying generalization in operant learning of professional basketball players. In particular, we utilize detailed information about the spatial organization of shot locations to study how players adapt their attacking strategy in real time according to recent events in the game. To quantify this learning, we study how a make \ miss from one location in the court affects the probabilities of shooting from different locations. We show that generalization is not a spatially-local process, nor is governed by the difficulty of the shot. Rather, to a first approximation, players use a simplified binary representation of the court into 2 pt and 3 pt zones. This result indicates that rather than using low-level features, generalization is determined by high-level cognitive processes that incorporate the abstract rules of the game.

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