IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-31727-0.html
   My bibliography  Save this article

Dynamic influences on static measures of metacognition

Author

Listed:
  • Kobe Desender

    (Brain and Cognition
    University Medical Center Hamburg-Eppendorf
    Ghent University)

  • Luc Vermeylen

    (Ghent University)

  • Tom Verguts

    (Ghent University)

Abstract

Humans differ in their capability to judge choice accuracy via confidence judgments. Popular signal detection theoretic measures of metacognition, such as M-ratio, do not consider the dynamics of decision making. This can be problematic if response caution is shifted to alter the tradeoff between speed and accuracy. Such shifts could induce unaccounted-for sources of variation in the assessment of metacognition. Instead, evidence accumulation frameworks consider decision making, including the computation of confidence, as a dynamic process unfolding over time. Using simulations, we show a relation between response caution and M-ratio. We then show the same pattern in human participants explicitly instructed to focus on speed or accuracy. Finally, this association between M-ratio and response caution is also present across four datasets without any reference towards speed. In contrast, when data are analyzed with a dynamic measure of metacognition, v-ratio, there is no effect of speed-accuracy tradeoff.

Suggested Citation

  • Kobe Desender & Luc Vermeylen & Tom Verguts, 2022. "Dynamic influences on static measures of metacognition," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31727-0
    DOI: 10.1038/s41467-022-31727-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-31727-0
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-31727-0?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. Annika Boldt & Vincent de Gardelle & Nick Yeung, 2017. "The impact of evidence reliability on sensitivity and bias in decision confidence," Post-Print hal-01659634, HAL.
    2. Florent Meyniel & Daniel Schlunegger & Stanislas Dehaene, 2015. "The Sense of Confidence during Probabilistic Learning: A Normative Account," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-25, June.
    3. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    4. Arbora Resulaj & Roozbeh Kiani & Daniel M. Wolpert & Michael N. Shadlen, 2009. "Changes of mind in decision-making," Nature, Nature, vol. 461(7261), pages 263-266, September.
    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. Manuel Rausch & Michael Zehetleitner, 2019. "The folded X-pattern is not necessarily a statistical signature of decision confidence," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-18, October.
    2. Marine Hainguerlot & Jean-Christophe Vergnaud & Vincent de Gardelle, 2018. "Metacognitive ability predicts learning cue-stimulus associations in the absence of external feedback," PSE-Ecole d'économie de Paris (Postprint) hal-01761531, HAL.
    3. Filipe Costa Souza & Wilton Bernardino & Silvio C. Patricio, 2024. "How life-table right-censoring affected the Brazilian social security factor: an application of the gamma-Gompertz-Makeham model," Journal of Population Research, Springer, vol. 41(3), pages 1-38, September.
    4. Fernando Rojas & Peter Wanke & Víctor Leiva & Mauricio Huerta & Carlos Martin-Barreiro, 2022. "Modeling Inventory Cost Savings and Supply Chain Success Factors: A Hybrid Robust Compromise Multi-Criteria Approach," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    5. Micha Heilbron & Florent Meyniel, 2019. "Confidence resets reveal hierarchical adaptive learning in humans," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-24, April.
    6. Zohar Z Bronfman & Noam Brezis & Marius Usher, 2016. "Non-monotonic Temporal-Weighting Indicates a Dynamically Modulated Evidence-Integration Mechanism," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-21, February.
    7. Villacorta, Pablo J. & Verdegay, José L., 2016. "FuzzyStatProb: An R Package for the Estimation of Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i08).
    8. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
    9. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).
    10. Safaei, Fatemeh & Taghipour, Sharareh, 2024. "Integrated degradation-based burn-in and maintenance model for heterogeneous and highly reliable items," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    11. William Lim & Gaurav Khemka & David Pitt & Bridget Browne, 2019. "A method for calculating the implied no-recovery three-state transition matrix using observable population mortality incidence and disability prevalence rates among the elderly," Journal of Population Research, Springer, vol. 36(3), pages 245-282, September.
    12. Shi, Yanlin, 2022. "A closed-form estimator for the Markov switching in mean model," Finance Research Letters, Elsevier, vol. 44(C).
    13. Olschewski, Sebastian & Diao, Linan & Rieskamp, Jörg, 2021. "Reinforcement learning about asset variability and correlation in repeated portfolio decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    14. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    15. Moraes, Ricardo Kalil & Wanke, Peter Fernandes & Faria, João Ricardo, 2021. "Unveiling the endogeneity between social-welfare and labor efficiency: Two-stage NDEA neural network approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    16. Adrian M Haith & David M Huberdeau & John W Krakauer, 2015. "Hedging Your Bets: Intermediate Movements as Optimal Behavior in the Context of an Incomplete Decision," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-21, March.
    17. Scheiter, Simon & Savadogo, Patrice, 2016. "Ecosystem management can mitigate vegetation shifts induced by climate change in West Africa," Ecological Modelling, Elsevier, vol. 332(C), pages 19-27.
    18. Andrea Insabato & Mario Pannunzi & Gustavo Deco, 2017. "Multiple Choice Neurodynamical Model of the Uncertain Option Task," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-29, January.
    19. Marchetti, Dalmo & Wanke, Peter, 2020. "Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    20. Sebastián Cano-Berlanga & José-Manuel Giménez-Gómez, 2018. "On Chinese stock markets: How have they evolved over time?," Annals of Operations Research, Springer, vol. 266(1), pages 499-510, 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:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31727-0. 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.nature.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.