Department of Neurobiology, The Interdisciplinary Center for Neural Computation and Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
Nat Commun. 2011 Dec 6;2:569. doi: 10.1038/ncomms1580.
Reinforcement learning in complex natural environments is a challenging task because the agent should generalize from the outcomes of actions taken in one state of the world to future actions in different states of the world. The extent to which human experts find the proper level of generalization is unclear. Here we show, using the sequences of field goal attempts made by professional basketball players, that the outcome of even a single field goal attempt has a considerable effect on the rate of subsequent 3 point shot attempts, in line with standard models of reinforcement learning. However, this change in behaviour is associated with negative correlations between the outcomes of successive field goal attempts. These results indicate that despite years of experience and high motivation, professional players overgeneralize from the outcomes of their most recent actions, which leads to decreased performance.
在复杂的自然环境中进行强化学习是一项具有挑战性的任务，因为代理需要将在一个世界状态下采取的行动的结果推广到未来在不同世界状态下的行动。人类专家在多大程度上能够找到适当的泛化程度尚不清楚。在这里，我们使用职业篮球运动员的投篮尝试序列表明，即使是单次投篮尝试的结果也会对随后的三分球尝试率产生相当大的影响，这与强化学习的标准模型一致。然而，这种行为的变化与连续投篮尝试结果之间的负相关有关。这些结果表明，尽管拥有多年的经验和高度的积极性，职业球员还是会从最近的行动结果中过度泛化，从而导致表现下降。
