To study the human mind is to consider the nature of associations—how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar of statistical learning (SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

中文翻译：

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统计学习对认知网络科学的价值


研究人类心灵就是要考虑联想的本质——它们是如何学习的，它们的组成部分是什么，以及如何切断或调整它们？关联的操纵是统计学习（SL）研究的支柱，它强烈表明，诸如分词、语法模式学习和事件感知等多样化的过程可以通过学习者对简单时间依赖性（以及其他规律）的敏感性来解释）。用于确定网络的边缘，在量化各种认知系统的图论属性时，关联同样至关重要。考虑到这一点，当前的工作重申了网络科学在阐明复杂认知系统的广泛架构方面的独特价值。然而，我还描述了来自 SL 文献的见解，加上更广泛的心理语言学的见解，如何提供强大的理论支柱，在此基础上我们可以开发和研究尽可能密切地反映学习心理现实的网络。