IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v554y2020ics0378437120301448.html
   My bibliography  Save this article

Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing

Author

Listed:
  • Stella, Massimo

Abstract

Concepts and their mental associations influence how language is processed and used. Networks represent powerful models for exploring such cognitive system, known as mental lexicon. This study investigates lexicon robustness to progressive word failure with multiplex network attacks. The average lexicon of an adult English speaker is built by considering 16000 words connected through semantic free associations and phonological sound similarities. Progressive structural degradation is modelled as random and targeted attacks. Words with higher psycholinguistic features (e.g. frequency, length, age of acquisition, polysemy) or network centrality (e.g. closeness, PageRank, betweenness and degree) are targeted first. Aphasia-inspired attacks are introduced here and target first words named correctly, more or less frequently, by patients with anomic aphasia, a pathology disrupting word finding. Robustness is measured as connectedness, fundamental for activation spreading and lexical retrieval, and viability, a multi-layer connectivity identifying language kernels. The lexicon is resilient to random, aphasia-inspired and psycholinguistic attacks. Catastrophic phase transitions happen when phonological and semantic degrees are combined, making the lexicon fragile to multidegree attacks. The viable kernel is fragile to multi-PageRank and to aphasia-inspired attacks. Consequently, connectedness in the lexicon is mediated by hubs, whereas viability enables a lexical semantic/phonological interplay and corresponds to a facilitative naming effect in aphasia. These effects persist also through ageing, in different network representations of younger and older lexicons. This study indicates the need to prevent failure of high multidegree and viable words in the mental lexicon when pursuing the design of effective language restoration strategies against cognitive impairing.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:554:y:2020:i:c:s0378437120301448
    DOI: 10.1016/j.physa.2020.124382
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120301448
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.124382?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jeffrey C. Zemla & Joseph L. Austerweil, 2019. "Analyzing Knowledge Retrieval Impairments Associated with Alzheimer’s Disease Using Network Analyses," Complexity, Hindawi, vol. 2019, pages 1-12, May.
    2. Karl D. Neergaard & Chu-Ren Huang, 2019. "Constructing the Mandarin Phonological Network: Novel Syllable Inventory Used to Identify Schematic Segmentation," Complexity, Hindawi, vol. 2019, pages 1-21, April.
    3. Massimo Stella, 2018. "Cohort and Rhyme Priming Emerge from the Multiplex Network Structure of the Mental Lexicon," Complexity, Hindawi, vol. 2018, pages 1-14, September.
    4. Terrill L. Frantz & Marcelo Cataldo & Kathleen M. Carley, 2009. "Robustness of centrality measures under uncertainty: Examining the role of network topology," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 303-328, December.
    5. 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.
    6. Manlio De Domenico & Albert Solé-Ribalta & Elisa Omodei & Sergio Gómez & Alex Arenas, 2015. "Ranking in interconnected multilayer networks reveals versatile nodes," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    2. Ciaglia, Floriana & Stella, Massimo & Kennington, Casey, 2023. "Investigating preferential acquisition and attachment in early word learning through cognitive, visual and latent multiplex lexical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antoaneta Serguieva & David Bholat, 2017. "Multichannel contagion vs stabilisation in multiple interconnected financial markets," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    2. Zhang, Ting & Zhang, Kun & Lv, Laishui & Bardou, Dalal, 2019. "Co-Ranking for nodes, layers and timestamps in multilayer temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 88-96.
    3. Mohsen Mosleh & Peter Ludlow & Babak Heydari, 2016. "Distributed Resource Management in Systems of Systems: An Architecture Perspective," Systems Engineering, John Wiley & Sons, vol. 19(4), pages 362-374, July.
    4. Wang, Zi-Yi & Han, Jing-Ti & Zhao, Jun, 2017. "Identifying node spreading influence for tunable clustering coefficient networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 242-250.
    5. Marco Bardoscia & Ginestra Bianconi & Gerardo Ferrara, 2019. "Multiplex network analysis of the UK over‐the‐counter derivatives market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1520-1544, October.
    6. 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.
    7. Jin, Haiyan & Zhang, ChenXing & Ma, Mengzhou & Gong, Qianhua & Yu, Liang & Guo, Xingli & Gao, Lin & Wang, Bingbo, 2020. "Inferring essential proteins from centrality in interconnected multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    8. Ferraz de Arruda, Henrique & Reia, Sandro Martinelli & Silva, Filipi Nascimento & Amancio, Diego Raphael & da Fontoura Costa, Luciano, 2022. "Finding contrasting patterns in rhythmic properties between prose and poetry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    9. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2022. "Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network," International Review of Financial Analysis, Elsevier, vol. 84(C).
    10. Junhui Cai & Dan Yang & Wu Zhu & Haipeng Shen & Linda Zhao, 2021. "Network regression and supervised centrality estimation," Papers 2111.12921, arXiv.org.
    11. Niu, Qikai & Zhou, Jianlin & Zeng, An & Fan, Ying & Di, Zengru, 2016. "Which publication is your representative work?," Journal of Informetrics, Elsevier, vol. 10(3), pages 842-853.
    12. Jianjun Lu & Shozo Tokinaga, 2013. "Analysis of cluster formations on planer cells based on genetic programming," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 426-445, December.
    13. Arun Advani & Bansi Malde, 2014. "Empirical methods for networks data: social effects, network formation and measurement error," IFS Working Papers W14/34, Institute for Fiscal Studies.
    14. Óskarsdóttir, María & Bravo, Cristián, 2021. "Multilayer network analysis for improved credit risk prediction," Omega, Elsevier, vol. 105(C).
    15. Annamaria Ficara & Giacomo Fiumara & Salvatore Catanese & Pasquale De Meo & Xiaoyang Liu, 2022. "The Whole Is Greater than the Sum of the Parts: A Multilayer Approach on Criminal Networks," Future Internet, MDPI, vol. 14(5), pages 1-21, April.
    16. 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.
    17. 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.
    18. 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).
    19. Antonin Bergeaud & Yoann Potiron & Juste Raimbault, 2017. "Classifying patents based on their semantic content," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-22, April.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:554:y:2020:i:c:s0378437120301448. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.