IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v1y2018i2d10.1007_s42001-018-0025-x.html
   My bibliography  Save this article

Model of cognitive dynamics predicts performance on standardized tests

Author

Listed:
  • Nathan O. Hodas

    (Pacific Northwest National Lab)

  • Jacob Hunter

    (Pacific Northwest National Lab)

  • Stephen J. Young

    (Pacific Northwest National Lab)

  • Kristina Lerman

    (USC Information Sciences Institute)

Abstract

In the modern knowledge economy, success demands sustained focus and high cognitive performance. Research suggests that human cognition is linked to a finite resource, and upon its depletion, cognitive functions such as self-control and decision-making may decline. While fatigue, among other factors, affects human activity, how cognitive performance evolves during extended periods of focus remains poorly understood. By analyzing performance of a large cohort answering practice standardized test questions online, we show that accuracy and learning decline as the test session progresses and recover following prolonged breaks. To explain these findings, we hypothesize that answering questions consumes some finite cognitive resources on which performance depends, but these resources recover during breaks between test questions. We propose a dynamic mechanism of the consumption and recovery of these resources and show that it explains empirical findings and predicts performance better than alternative hypotheses. While further controlled experiments are needed to identify the physiological origin of these phenomena, our work highlights the potential of empirical analysis of large-scale human behavior data to explore cognitive behavior.

Suggested Citation

  • Nathan O. Hodas & Jacob Hunter & Stephen J. Young & Kristina Lerman, 2018. "Model of cognitive dynamics predicts performance on standardized tests," Journal of Computational Social Science, Springer, vol. 1(2), pages 295-312, September.
  • Handle: RePEc:spr:jcsosc:v:1:y:2018:i:2:d:10.1007_s42001-018-0025-x
    DOI: 10.1007/s42001-018-0025-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-018-0025-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s42001-018-0025-x?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. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    2. Philipp Singer & Emilio Ferrara & Farshad Kooti & Markus Strohmaier & Kristina Lerman, 2016. "Evidence of Online Performance Deterioration in User Sessions on Reddit," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    Full references (including those not matched with items on IDEAS)

    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. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    2. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    3. Moshe B Hoshen & Anthony H Burton & Themis J V Bowcock, 2007. "Simulating disease transmission dynamics at a multi-scale level," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 26-34.
    4. Luc E. Coffeng & Sake J. de Vlas, 2022. "Predicting epidemics and the impact of interventions in heterogeneous settings: Standard SEIR models are too pessimistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 28-35, November.
    5. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    6. Kris V. Parag & Robin N. Thompson & Christl A. Donnelly, 2022. "Are epidemic growth rates more informative than reproduction numbers?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 5-15, November.
    7. Thomas Ash & Antonio M. Bento & Daniel Kaffine & Akhil Rao & Ana I. Bento, 2022. "Disease-economy trade-offs under alternative epidemic control strategies," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Maarten Jan Wensink & Linda Juel Ahrenfeldt & Sören Möller, 2020. "Variability Matters," IJERPH, MDPI, vol. 18(1), pages 1-8, December.
    9. Lingcai Kong & Jinfeng Wang & Weiguo Han & Zhidong Cao, 2016. "Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model," IJERPH, MDPI, vol. 13(3), pages 1-13, February.
    10. Carolyn Ingram & Vicky Downey & Mark Roe & Yanbing Chen & Mary Archibald & Kadri-Ann Kallas & Jaspal Kumar & Peter Naughton & Cyril Onwuelazu Uteh & Alejandro Rojas-Chaves & Shibu Shrestha & Shiraz Sy, 2021. "COVID-19 Prevention and Control Measures in Workplace Settings: A Rapid Review and Meta-Analysis," IJERPH, MDPI, vol. 18(15), pages 1-26, July.
    11. Wayne M. Getz & Jean-Paul Gonzalez & Richard Salter & James Bangura & Colin Carlson & Moinya Coomber & Eric Dougherty & David Kargbo & Nathan D. Wolfe & Nadia Wauquier, 2015. "Tactics and Strategies for Managing Ebola Outbreaks and the Salience of Immunization," Post-Print hal-01214432, HAL.
    12. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2016. "Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-18, April.
    13. Kathrin Büttner & Joachim Krieter & Arne Traulsen & Imke Traulsen, 2013. "Efficient Interruption of Infection Chains by Targeted Removal of Central Holdings in an Animal Trade Network," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    14. Ellen Brooks-Pollock & Leon Danon & Hester Korthals Altes & Jennifer A Davidson & Andrew M T Pollock & Dick van Soolingen & Colin Campbell & Maeve K Lalor, 2020. "A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-14, March.
    15. Jonas I Liechti & Gabriel E Leventhal & Sebastian Bonhoeffer, 2017. "Host population structure impedes reversion to drug sensitivity after discontinuation of treatment," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-19, August.
    16. T Alex Perkins & Thomas W Scott & Arnaud Le Menach & David L Smith, 2013. "Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-16, December.
    17. Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.
    18. Mark D Jankowski & Christopher J Williams & Jeanne M Fair & Jennifer C Owen, 2013. "Birds Shed RNA-Viruses According to the Pareto Principle," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    19. Yunhwan Kim & Hohyung Ryu & Sunmi Lee, 2018. "Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea," IJERPH, MDPI, vol. 15(11), pages 1-17, October.
    20. Anna C Peterson & Valerie J McKenzie, 2014. "Investigating Differences across Host Species and Scales to Explain the Distribution of the Amphibian Pathogen Batrachochytrium dendrobatidis," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-15, September.

    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:spr:jcsosc:v:1:y:2018:i:2:d:10.1007_s42001-018-0025-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.