Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process

Castro, Nichol; Siew, Cynthia S. Q.

doi:10.1098/rspa.2019.0825

Cited by 58 publications

(49 citation statements)

References 139 publications

(203 reference statements)

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“…A further limitation is that we interpret our findings of the benefits of reduced distances in a cognitive multilayer network architecture in facilitating communication efficiency, without directly testing for such dynamics. However, current cognitive network research is only recently starting to uncover and examine such cognitive dynamics, based on similar methods to those we applied in our work 17 , 57 . Much more research is needed to advance our understanding of such cognitive dynamics over memory and linguistic networks, making such work outside the scope of our current study.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks

Levy

Kenett

Oxenberg

et al. 2021

Sci Rep

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An essential aspect of human communication is the ability to access and retrieve information from ones’ ‘mental lexicon’. This lexical access activates phonological and semantic components of concepts, yet the question whether and how these two components relate to each other remains widely debated. We harness tools from network science to construct a large-scale linguistic multilayer network comprising of phonological and semantic layers. We find that the links in the two layers are highly similar to each other and that adding information from one layer to the other increases efficiency by decreasing the network overall distances, but specifically affecting shorter distances. Finally, we show how a multilayer architecture demonstrates the highest efficiency, and how this efficiency relates to weak semantic relations between cue words in the network. Thus, investigating the interaction between the layers and the unique benefit of a linguistic multilayer architecture allows us to quantify theoretical cognitive models of lexical access.

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Section: Discussionmentioning

confidence: 99%

Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks

Levy

Kenett

Oxenberg

et al. 2021

Sci Rep

Self Cite

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“…Similarly, networks can represent the complexities of word acquisition to the lexicon (e.g., Hills, Maouene, Maouene, Sheya & Smith, 2009) and may easily be extended to the study of bilingual language learning. Other areas of research within bilingualism, such as emotional word processing, may also benefit from a formal network representation, as some are beginning to do with concept development (e.g., Castro & Siew, 2019;Siew, Wulff, Beckage, & Kennett, 2019).…”

Section: Discussionmentioning

confidence: 99%

Using Network Science to Map What Montréal Bilinguals Talk about Across Languages and Communicative Contexts

Tiv¹,

Gullifer²,

Feng³

et al. 2020

Preprint

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Recent work within the language sciences, particularly bilingualism, has sought new methods to evaluate and characterize how people differentially use language across different communicative contexts. These differences have thus far been linked to changes in cognitive control strategy, reading behavior, and brain organization. Here, we approach this issue using a novel application of Network Science to map the conversational topics that Montréal bilinguals discuss across communicative contexts (e.g., work, home, family, school, social), in their dominant vs. non-dominant language. Our results demonstrate that all communicative contexts display a unique pattern in which conversational topics are discussed, but only a few communicative contexts (work and social) display a unique pattern of how many languages are used to discuss particular topics. We also demonstrate that the dominant language has greater network size, strength, and density than the non-dominant language, suggesting that more topics are used in a wider variety of contexts in this language. Lastly, using community detection to thematically group the topics in each language, we find evidence of greater specificity in the non-dominant language than the dominant language. We contend that Network Science is a valuable tool for representing complex information, such as individual differences in bilingual language use, in a rich and granular manner, that may be used to better understand brain and behavior.

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“…They are a major tool for the analysis of interacting components in real networked systems with different types of interactions. A diverse range of applications have been found in, for example, synchronization problems [27], interbank market [28], social contagions [29], cognitive psychology [30], molecular biology [31], and community detection [32], to name just a few. Structural properties of multiplex networks, including core [33], communicability [34], and isomorphism [35], have attracted much attention from the perspective of network theory and often represent an essential extension to their counterparts in classical graph theory.…”

Section: A Graph Theorymentioning

confidence: 99%

Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals

Shang

2021

IEEE Trans. Netw. Sci. Eng.

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The consensus problem with asymmetric confidence intervals considered in this paper is characterized by the fact that each agent can have optimistic and/or pessimistic interactions with its neighbors. To deal with the asymmetric confidence scenarios, we introduce a novel multiplex network presentation for directed graphs and its associated connectivity concepts including the pseudo-strongly connectivity and graph robustness, which provide a resilience characterization in the presence of malicious nodes. We develop distributed resilient consensus strategies for a group of dynamical agents with locally bounded Byzantine agents in both continuous-time and discrete-time multi-agent systems. Drawing on our multiplex network framework, much milder connectivity conditions compared to existing works are proposed to ensure resilient consensus. The results are further extended to cope with resilient scaled consensus problems which allow both cooperative and antagonistic agreements among agents. Numerical examples are also exhibited to confirm the theoretical results and reveal the factors that affect the speed of convergence in our multiplex network framework.

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Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process

Cited by 58 publications

References 139 publications

Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks

Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks

Using Network Science to Map What Montréal Bilinguals Talk about Across Languages and Communicative Contexts

Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals

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