论文标题

多层网络的认知建模:见解,进步和未来挑战

Cognitive modelling with multilayer networks: Insights, advancements and future challenges

论文作者

Stella, Massimo, Citraro, Salvatore, Rossetti, Giulio, Marinazzo, Daniele, Kenett, Yoed N., Vitevitch, Michael S.

论文摘要

精神词典是一个复杂的认知系统,代表了人们知道的单词/概念的信息。数十年的心理实验表明,跨多个交互式认知水平的概念关联可以极大地影响单词获取,存储和处理。语义,语音,句法和其他类型的概念关联如何在连贯的数学框架中映射以研究精神词典的工作方式?我们在这里回顾认知多层网络是研究精神词典的有希望的定量和解释框架。认知多层网络可以一次映射多种类型的信息,从而捕获不同的关联层如何在精神词典中共存并影响认知处理。这篇评论从对多层网络的结构和形式主义的温和介绍开始。然后,我们讨论了心理现象的定量机制,这些机制在单层网络中无法观察到,并且仅通过结合词典的多层层来揭示:(i)多重可行性突出了语言内核和知识处理在健康和临床种群中的知识处理效果; (ii)多层社区检测可以取决于心理语言特征的上下文含义重建; (iii)层分析可以介导调解,抑制和促进词的潜在相互作用。通过概述多层网络可以阐明认知知识表示形式的新颖定量观点(在下一代大脑/思维模型中),我们讨论了关键的局限性和有前途的未来研究方向。

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? We here review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, also in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

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