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

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  • Cynthia S. Q. Siew
  • Dirk U. Wulff
  • Nicole M. Beckage
  • Yoed N. Kenett

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.

Suggested Citation

  • Cynthia S. Q. Siew & Dirk U. Wulff & Nicole M. Beckage & Yoed N. Kenett, 2019. "Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics," Complexity, Hindawi, vol. 2019, pages 1-24, June.
  • Handle: RePEc:hin:complx:2108423
    DOI: 10.1155/2019/2108423
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    References listed on IDEAS

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    Cited by:

    1. Olga Valba & Alexander Gorsky & Sergei Nechaev & Mikhail Tamm, 2021. "Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    2. Xuefang Feng & Jie Liu, 2023. "The developmental trajectories of L2 lexical-semantic networks," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    3. Ammar Al-Sharaa & Mastura Adam & Amer Siddiq Amer Nordin & Riyadh Mundher & Ameer Alhasan, 2022. "Assessment of Wayfinding Performance in Complex Healthcare Facilities: A Conceptual Framework," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    4. Joseph, Simmi Marina & Citraro, Salvatore & Morini, Virginia & Rossetti, Giulio & Stella, Massimo, 2023. "Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    5. Yoshiaki Fujita & Michael S. Vitevitch, 2022. "Using network analyses to examine the extent to which and in what ways psychology is multidisciplinary," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    6. Brian, Kieran & Stella, Massimo, 2023. "Introducing mindset streams to investigate stances towards STEM in high school students and experts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    7. Stefan Claus & Massimo Stella, 2022. "Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts," Future Internet, MDPI, vol. 14(10), pages 1-18, October.
    8. Stella, Massimo, 2020. "Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).

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