Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Sci Rep. 2017 Oct 6;7(1):12733. doi: 10.1038/s41598-017-12876-5.
Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of the world around us. How human learners exploit this information remains an essential question. Here, we focus on the temporal constraints that govern such a process. Participants viewed a continuous sequence of images generated by three distinct walks on a modular network. Walks varied along two critical dimensions: their predictability and the density with which they sampled from communities of images. Learners exposed to walks that richly sampled from each community exhibited a sharp increase in processing time upon entry into a new community. This effect was eliminated in a highly regular walk that sampled exhaustively from images in short, successive cycles (i.e., that increasingly minimized uncertainty about the nature of upcoming stimuli). These results demonstrate that temporal organization plays an essential role in learners' sensitivity to the network architecture underlying sensory input.
网络科学已经成为一种强大的工具，通过它我们可以研究我们周围世界的高阶结构属性。人类学习者如何利用这些信息仍然是一个重要的问题。在这里，我们关注的是控制这一过程的时间约束。参与者观看了由模块化网络上三个不同的行走生成的连续图像序列。行走在两个关键维度上有所不同：它们的可预测性以及它们从图像社区中采样的密集程度。接触到从每个社区中大量采样的行走的学习者，在进入新社区时，处理时间会急剧增加。在一个高度规则的行走中，这种效应被消除了，该行走从短而连续的周期中（即，越来越小化了对后续刺激性质的不确定性）中对图像进行了详尽的采样。这些结果表明，时间组织在学习者对感官输入基础的网络结构的敏感性中起着至关重要的作用。
