In this article we address two related issues on the learning of probabilistic sequences of events. First, which features make the sequence of events generated by a stochastic chain more difficult to predict. Second, how to model the procedures employed by different learners to identify the structure of sequences of events. Playing the role of a goalkeeper in a video game, participants were told to predict step by step the successive directions -- left, center or right -- to which the penalty kicker would send the ball. The sequence of kicks was driven by a stochastic chain with memory of variable length. Results showed that at least three features play a role in the first issue: 1) the shape of the context tree summarizing the dependencies between present and past directions; 2) the entropy of the stochastic chain used to generate the sequences of events; 3) the existence or not of a deterministic periodic sequence underlying the sequences of events. Moreover, evidence suggests that best learners rely less on their own past choices to identify the structure of the sequences of events.

翻译：在这篇文章中，我们探讨了两个与学习概率事件序列相关的问题。首先，哪些特征会使随机链生成的事件序列更难预测。其次，如何建模不同学习者用于识别事件序列结构的过程。在一个视频游戏中扮演守门员的角色，参与者被告知要逐步预测罚球手将把球发送到哪个方向--左、中或右。罚球顺序由具有可变记忆长度的随机链驱动。结果表明，至少有三个特征在第一个问题中起着作用：1）总结-present和past方向之间依赖关系的上下文树的形状；2）用于生成事件序列的随机链的熵；3）序列中存在或不存在确定性周期序列。此外，研究结果表明，最好的学习者在识别事件序列结构时越少依赖自己的过去选择。