IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0248986.html
   My bibliography  Save this article

Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests

Author

Listed:
  • Olga Valba
  • Alexander Gorsky
  • Sergei Nechaev
  • Mikhail Tamm

Abstract

We study correlations between the structure and properties of a free association network of the English language, and solutions of psycholinguistic Remote Association Tests (RATs). We show that average hardness of individual RATs is largely determined by relative positions of test words (stimuli and response) on the free association network. We argue that the solution of RATs can be interpreted as a first passage search problem on a network whose vertices are words and links are associations between words. We propose different heuristic search algorithms and demonstrate that in “easily-solving” RATs (those that are solved in 15 seconds by more than 64% subjects) the solution is governed by “strong” network links (i.e. strong associations) directly connecting stimuli and response, and thus the efficient strategy consist in activating such strong links. In turn, the most efficient mechanism of solving medium and hard RATs consists of preferentially following sequence of “moderately weak” associations.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0248986
    DOI: 10.1371/journal.pone.0248986
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248986
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248986&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0248986?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
    ---><---

    References listed on IDEAS

    as
    1. Fragkiskos Papadopoulos & Maksim Kitsak & M. Ángeles Serrano & Marián Boguñá & Dmitri Krioukov, 2012. "Popularity versus similarity in growing networks," Nature, Nature, vol. 489(7417), pages 537-540, September.
    2. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    3. 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.
    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. 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).

    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. Jascha-Alexander Koch & Michael Siering, 2019. "The recipe of successful crowdfunding campaigns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(4), pages 661-679, December.
    2. Sumeet Kumar & Binxuan Huang & Ramon Alfonso Villa Cox & Kathleen M. Carley, 2021. "An anatomical comparison of fake-news and trusted-news sharing pattern on Twitter," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 109-133, June.
    3. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    4. Rutten, Philip & Lees, Michael H. & Klous, Sander & Sloot, Peter M.A., 2021. "Intermittent and persistent movement patterns of dance event visitors in large sporting venues," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    5. Jovanovic, Franck & Schinckus, Christophe, 2016. "Breaking down the barriers between econophysics and financial economics," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 256-266.
    6. Khalilzadeh, Jalayer, 2022. "It is a small world, or is it? A look into two decades of tourism system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    7. Shu Takahashi & Kento Yamamoto & Shumpei Kobayashi & Ryoma Kondo & Ryohei Hisano, 2024. "Dynamic Link and Flow Prediction in Bank Transfer Networks," Papers 2409.08718, arXiv.org, revised Oct 2024.
    8. Li, Heyang & Wu, Meijun & Wang, Yougui & Zeng, An, 2022. "Bibliographic coupling networks reveal the advantage of diversification in scientific projects," Journal of Informetrics, Elsevier, vol. 16(3).
    9. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    10. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," Papers 1808.08585, arXiv.org.
    11. Katahira, Kei & Chen, Yu & Akiyama, Eizo, 2021. "Self-organized Speculation Game for the spontaneous emergence of financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    12. Yang, Alex Jie & Wu, Linwei & Zhang, Qi & Wang, Hao & Deng, Sanhong, 2023. "The k-step h-index in citation networks at the paper, author, and institution levels," Journal of Informetrics, Elsevier, vol. 17(4).
    13. Wang, Xuhui & Wu, Jiao & Yang, Zheng & Xu, Kesheng & Wang, Zhengling & Zheng, Muhua, 2024. "The correlation between independent edge and triangle degrees promote the explosive information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    14. Makoto Takeuchi & Yukie Sano, 2024. "Burstiness of human physical activities and their characterisation," Journal of Computational Social Science, Springer, vol. 7(1), pages 625-641, April.
    15. Xizhe Zhang & Tianyang Lv & XueYing Yang & Bin Zhang, 2014. "Structural Controllability of Complex Networks Based on Preferential Matching," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-8, November.
    16. Behrouzi, Saman & Shafaeipour Sarmoor, Zahra & Hajsadeghi, Khosrow & Kavousi, Kaveh, 2020. "Predicting scientific research trends based on link prediction in keyword networks," Journal of Informetrics, Elsevier, vol. 14(4).
    17. Hadi Arbabi & Martin Mayfield & Philip McCann, 2020. "Productivity, infrastructure and urban density—an allometric comparison of three European city regions across scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 211-228, January.
    18. Yu Zhang & Claudio Tessone, 2024. "Bitcoin Transaction Behavior Modeling Based on Balance Data," Papers 2409.10407, arXiv.org.
    19. He, Fang & Chen, Xi, 2016. "Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 158-170.
    20. Juan Miguel Carrascosa & Ruben Cuevas & Roberto Gonzalez & Arturo Azcorra & David Garcia, 2015. "Quantifying the Economic and Cultural Biases of Social Media through Trending Topics," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-14, July.

    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:plo:pone00:0248986. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.