MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University, Amsterdam, The Netherlands.
PLoS One. 2013 Apr 24;8(4):e62276. doi: 10.1371/journal.pone.0062276. Print 2013.
Recent work has shown that humans can learn or detect complex dependencies among variables. Even learning a simple dependency involves the identification of an underlying model and the learning of its parameters. This process represents learning a structured problem. We are interested in an empirical assessment of some of the factors that enable humans to learn such a dependency over time. More specifically, we look at how the statistics of the presentation of samples from a given structure influence learning. Participants engage in an experimental task where they are required to predict the timing of a target. At the outset, they are oblivious to the existence of a relationship between the position of a stimulus and the required temporal response to intercept it. Different groups of participants are either presented with a Random Walk where consecutive stimuli were correlated or with stimuli that were uncorrelated over time. We find that the structural relationship implicit in the task is only learned in the conditions where the stimuli are independently drawn. This leads us to believe that humans require rich and independent sampling to learn hidden structures among variables.
最近的研究表明，人类可以学习或检测变量之间的复杂依赖关系。即使学习一个简单的依赖关系也涉及到识别一个潜在的模型和学习其参数。这个过程代表了学习一个结构化的问题。我们感兴趣的是对一些能够使人类随着时间的推移学习这种依赖关系的因素进行实证评估。更具体地说，我们研究了呈现给定结构的样本的统计数据如何影响学习。参与者参与一项实验任务，要求他们预测目标的时间。在开始时，他们并不知道刺激的位置和拦截它所需的时间响应之间存在关系。不同组的参与者要么呈现连续刺激相关的随机游走，要么呈现随时间不相关的刺激。我们发现，任务中隐含的结构关系只有在刺激独立抽取的情况下才会被学习。这使我们相信，人类需要丰富和独立的采样来学习变量之间的隐藏结构。
