Wang Rui, Shen Yuan, Tino Peter, Welchman Andrew E, Kourtzi Zoe
Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Department of Psychology, University of Cambridge, Cambridge, UK.
J Vis. 2017 Oct 1;17(12):1. doi: 10.1167/17.12.1.
Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.
人类行为受我们对未来的期望所引导。通常，我们通过监测事件序列如何展开来进行预测，即使这些序列可能看起来难以理解。自然环境中的事件结构通常在复杂性上有所不同，从简单的重复到复杂的概率组合。我们是如何学习这些结构的呢？在这里，我们通过追踪人类对结构在参与者不知情的情况下发生变化的时间序列的反应，来研究结构学习的动态过程。参与者被要求根据一个符号的概率序列预测下一个出现的项目。我们使用马尔可夫过程创建了一系列序列，从简单的频率统计（例如，某些符号比其他符号更有可能出现）到基于上下文的统计（例如，符号概率取决于前面的符号）。我们展示了个体适应环境统计变化的动态过程——也就是说，他们提取与行为相关的结构来预测即将发生的事件。此外，我们表明这种结构学习与个体决策策略有关；更快地学习复杂结构与在给定情境中选择最可能的结果（最大化）有关，而不是与精确的序列统计相匹配。我们的研究结果为学习与行为相关的统计数据提供了替代途径的证据，这些途径有助于我们在多变的环境中预测未来事件的能力。
