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

TL;DR

This paper reviews the use of multilayer network models to understand the complex structure of the mental lexicon, highlighting how multiple types of associations influence language processing and proposing future research directions.

Contribution

It introduces multilayer networks as a comprehensive framework for modeling the mental lexicon, demonstrating their ability to reveal phenomena not observable in single-layer models.

Findings

Multilayer networks uncover language kernels and facilitative effects.

Community detection enables contextual meaning reconstruction.

Layer analysis reveals interactions affecting lexical access.

Abstract

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.