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：本文探讨了关于概率事件序列学习的两个相关问题：第一，由随机链生成的事件序列中哪些特征使其更难以预测；第二，如何对不同学习者用于识别事件序列结构的程序进行建模。在视频游戏中扮演守门员的参与者被要求逐步预测罚球者将球踢向连续方向（左、中、右）的走向。射门序列由一个具有可变记忆长度的随机链驱动。结果表明，至少三个特征在第一个问题中发挥作用：1) 总结当前与过去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) 事件序列底层是否存在确定性周期序列。此外，证据表明，最佳学习者较少依赖自身过去的选择来识别事件序列结构。