Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics

Abstract

Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three key points. (i) Network science provides a powerful quantitative approach to represent cognitive systems. (ii) The network science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying network, and processes operating on a network structure interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive network science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.

Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

“…Furthermore, we examine how well a machine learning approach, based on semantic fluency and multiplex lexical networks, can classify participants into groups that relate to their creativity level. In line with recent literature considering proficiency in linguistic tasks [11,12,69] and fluency in particular [5,20,39,40] as the outcome of a network-based mental exploration of the mental lexicon, we quantify performance in generating fluency lists in terms of both performance-based (i.e., length of semantic fluency list) and network-based features (i.e., reliance on the LVC).…”

“…The resulting multiplex network included aspects of the mental lexicon such as phoneme overlap, meaning sharing, hierarchical generalizations, and semantic memory patterns, which were all reported to influence language processing and acquisition in both healthy [11,14,44,61,62] and clinical [46,48,51] populations. Based on recent results demonstrating how semantic memory structure relates to individual differences in creativity [20], we assume that the multilayer, networked representation of the mental lexicon serves as a valid approach to assess differences across our two levels of creativity groups.…”

“…In the current study we apply a computational multiplex network analysis [9,10] to examine how low and high creative individuals search through their memory to retrieve animal names. We outline one such approach, based on the application of network science methodologies [11]. Network science is based on mathematical graph theory and allows quantification of complex systems as networks.…”

“…Network science is based on mathematical graph theory and allows quantification of complex systems as networks. The application of network science to study cognitive phenomena is steadily developing, providing quantitative means to study the structure and dynamics of cognitive systems [11] and the interplay between cognition and language in terms of modelling word learning [12], picture naming [13], semantic relatedness [14], stance detection [15], and personality traits [16]. Recently, computational methods to study knowledge and memory structure in creativity are paving the way to uniquely examine their role in the creative process [17][18][19][20] and examine the associative theory of creativity [5,6].…”

Forma mentis networks quantify crucial differences in STEM perception between students and experts

“…Beyond supervised learning, network models stand as a promising avenue to the investigation of cognitive and linguistic data, leading to the emergence of the field of cognitive network science (Siew, Wulff, Beckage, & Kenett, 2018). Network models of language are often interpreted as descriptive representations of the mental lexicon, a repository of linguistic and semantic knowledge in human memory (Aitchison, 2012).…”