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：在本文中,我们讨论结构学习的两个相关问题。首先,它使由随机链产生的事件序列更难预测。第二,如何模拟不同学习者用来确定事件序列结构的程序。在视频游戏中,作为守门员的角色,让参与者一步一步地预测惩罚踢手发送球的先后方向 -- -- 左、中或右 -- -- 。踢球的顺序是由具有可变长度记忆的随机链驱动的。结果显示,至少有三个特征在第一个问题中发挥作用:1) 概述当前和过去方向之间依赖关系的上下文树形状;2) 用于产生事件序列的断层链的宽度;3) 事件序列背后的固定周期序列的存在与否。此外,证据表明,最佳学习者不那么依赖自己的过去选择来确定事件序列的结构。</s>