In this article we address two related issues in structural learning. 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：本文探讨结构学习中的两个相关问题：第一，哪些特征使随机链生成的事件序列更难以预测；第二，如何对不同学习者识别事件序列结构的过程进行建模。参与者需在电子游戏中扮演守门员，逐步预测罚点球者将球踢向左侧、中间或右侧的连续方向。踢球序列由具有可变长度记忆的随机链驱动。结果表明，至少三个特征对第一个问题发挥作用：1) 概括当前与过去方向之间依赖关系的上下文树形状；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往选择来识别事件序列的结构。