Some of the earliest work on understanding how concepts are organized in memory used a network-based approach, where words or concepts are represented as nodes, and relationships between words are represented by links between nodes. Over the past two decades, advances in network science and graph theoretical methods have led to the development of computational semantic networks. This review provides a modern perspective on how computational semantic networks have proven to be useful tools to investigate the structure of semantic memory as well as search and retrieval processes within semantic memory, to ultimately model performance in a wide variety of cognitive tasks. Regarding representation, the review focuses on the distinctions and similarities between network-based (based on behavioral norms) approaches and more recent distributional (based on natural language corpora) semantic models, and the potential overlap between the two approaches. Capturing the type of relation between concepts appears to be particularly important in this modeling endeavor. Regarding processes, the review focuses on random walk models and the degree to which retrieval processes demand attention in pursuit of given task goals, which dovetails with the type of relation retrieved during tasks. Ultimately, this review provides a critical assessment of how the network perspective can be reconciled with distributional and machine-learning-based perspectives to meaning representation, and describes how cognitive network science provides a useful conceptual toolkit to probe both the structure and retrieval processes within semantic memory.

中文翻译：

---

对语义记忆结构和过程的基于网络和分布方法的批判性评论

一些关于理解如何在记忆中组织概念的最早工作使用基于网络的方法，其中单词或概念表示为节点，单词之间的关系由节点之间的链接表示。在过去的二十年里，网络科学和图论方法的进步导致了计算语义网络的发展。这篇综述提供了一个关于计算语义网络如何被证明是研究语义记忆结构以及搜索和检索过程的有用工具的现代视角在语义记忆中，最终对各种认知任务的表现进行建模。关于表示，该评论侧重于基于网络（基于行为规范）方法与最近的分布式（基于自然语言语料库）语义模型之间的区别和相似之处，以及这两种方法之间的潜在重叠。捕获概念之间的关系类型在此建模工作中似乎尤为重要。关于流程，审查的重点是随机游走模型以及检索过程在追求给定任务目标时需要注意的程度，这与任务期间检索到的关系类型相吻合。最终，