IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0311295.html
   My bibliography  Save this article

Decomposing decision-making in preschoolers: Making decisions under ambiguity versus risk

Author

Listed:
  • Nancy Garon
  • Ellen Doucet
  • Bronwyn Inness

Abstract

Decision-making in the real world involves multiple abilities. The main goal of the current study was to examine the abilities underlying the Preschool Gambling task (PGT), a preschool variant of the Iowa Gambling task (IGT), in the context of an integrative decision-making framework. Preschoolers (n = 144) were given the PGT along with four novel decision-making tasks assessing either decision-making under ambiguity or decision-making under risk. Results indicated that the ability to learn from feedback, to maintain a stable preference, and to integrate losses and gains contributed to the variance in decision-making on the PGT. Furthermore, children’s awareness level on the PGT contributed additional variance, suggesting both implicit and explicit processes are involved. The results partially support the integrative decision-making framework and suggest that multiple abilities contribute to individual differences in decision-making on the PGT.

Suggested Citation

  • Nancy Garon & Ellen Doucet & Bronwyn Inness, 2024. "Decomposing decision-making in preschoolers: Making decisions under ambiguity versus risk," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-31, September.
    • Handle: RePEc:plo:pone00:0311295
    DOI: 10.1371/journal.pone.0311295
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0311295
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0311295&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0311295?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. Nathaniel D. Daw & John P. O'Doherty & Peter Dayan & Ben Seymour & Raymond J. Dolan, 2006. "Cortical substrates for exploratory decisions in humans," Nature, Nature, vol. 441(7095), pages 876-879, June.
    2. repec:cup:judgdm:v:9:y:2014:i:3:p:274-286 is not listed on IDEAS
    3. Leonard Lee & On Amir & Dan Ariely, 2009. "In Search of Homo Economicus: Cognitive Noise and the Role of Emotion in Preference Consistency," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 36(2), pages 173-187.
    4. Irwin P. Levin & Elaine A. Bossard & Gary J. Gaeth & Haoyang Yan, 2014. "The combined role of task, child's age and individual differences in understanding decision processes," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(3), pages 274-286, May.
    5. Vered Kurtz-David & Dotan Persitz & Ryan Webb & Dino J. Levy, 2019. "The neural computation of inconsistent choice behavior," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    6. Levin, Irwin P. & Bossard, Elaine A. & Gaeth, Gary J. & Yan, Haoyang, 2014. "The combined role of task, child’s age and individual differences in understanding decision processes," Judgment and Decision Making, Cambridge University Press, vol. 9(3), pages 273-285, May.
    7. repec:plo:pcbi00:1006989 is not listed on IDEAS
    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. Sebastian Lehmann & Martin Reimann, 2012. "The influence of time and money on product evaluations - a neurophysiological analysis," FEMM Working Papers 120011, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    2. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    3. Shi, Yuwei & Herniman, John, 2023. "The role of expectation in innovation evolution: Exploring hype cycles," Technovation, Elsevier, vol. 119(C).
    4. Moshe Glickman & Orian Sharoni & Dino J Levy & Ernst Niebur & Veit Stuphorn & Marius Usher, 2019. "The formation of preference in risky choice," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-25, August.
    5. Sashittal, Hemant C. & Sriramachandramurthy, Rajendran & Hodis, Monica, 2012. "Targeting college students on Facebook? How to stop wasting your money," Business Horizons, Elsevier, vol. 55(5), pages 495-507.
    6. Peter S. Riefer & Bradley C. Love, 2015. "Unfazed by Both the Bull and Bear: Strategic Exploration in Dynamic Environments," Games, MDPI, vol. 6(3), pages 1-11, August.
    7. Makoto Naruse & Eiji Yamamoto & Takashi Nakao & Takuma Akimoto & Hayato Saigo & Kazuya Okamura & Izumi Ojima & Georg Northoff & Hirokazu Hori, 2018. "Why is the environment important for decision making? Local reservoir model for choice-based learning," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-17, October.
    8. Jeremy Gordon & Flavio Chierichetti & Alessandro Panconesi & Giovanni Pezzulo, 2023. "Information foraging with an oracle," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-21, December.
    9. Adam, Marc T.P. & Krämer, Jan & Müller, Marius B., 2015. "Auction Fever! How Time Pressure and Social Competition Affect Bidders’ Arousal and Bids in Retail Auctions," Journal of Retailing, Elsevier, vol. 91(3), pages 468-485.
    10. Christina Fang & Daniel Levinthal, 2009. "Near-Term Liability of Exploitation: Exploration and Exploitation in Multistage Problems," Organization Science, INFORMS, vol. 20(3), pages 538-551, June.
    11. Aydinli, Aylin & Gu, Yangjie & Pham, Michel Tuan, 2017. "An experience-utility explanation of the preference for larger assortments," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 746-760.
    12. Hu, Yingyao & Kayaba, Yutaka & Shum, Matthew, 2013. "Nonparametric learning rules from bandit experiments: The eyes have it!," Games and Economic Behavior, Elsevier, vol. 81(C), pages 215-231.
    13. Michele Garagnani, 2023. "The predictive power of risk elicitation tasks," Journal of Risk and Uncertainty, Springer, vol. 67(2), pages 165-192, October.
    14. Jean Daunizeau & Hanneke E M den Ouden & Matthias Pessiglione & Stefan J Kiebel & Klaas E Stephan & Karl J Friston, 2010. "Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-10, December.
    15. Lamberto Zollo, 2021. "The Consumers’ Emotional Dog Learns to Persuade Its Rational Tail: Toward a Social Intuitionist Framework of Ethical Consumption," Journal of Business Ethics, Springer, vol. 168(2), pages 295-313, January.
    16. Darren W Dahl & Eileen Fischer & Gita V Johar & Vicki G Morwitz, 2017. "Making Sense from (Apparent) Senselessness: The JCR Lens," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(4), pages 719-723.
    17. Elyse H Norton & Stephen M Fleming & Nathaniel D Daw & Michael S Landy, 2017. "Suboptimal Criterion Learning in Static and Dynamic Environments," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-28, January.
    18. Ayaka Kato & Kenji Morita, 2016. "Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-41, October.
    19. Raphael Thomadsen & Robert P. Rooderkerk & On Amir & Neeraj Arora & Bryan Bollinger & Karsten Hansen & Leslie John & Wendy Liu & Aner Sela & Vishal Singh & K. Sudhir & Wendy Wood, 2018. "How Context Affects Choice," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 3-14, March.
    20. Elise Payzan-LeNestour & Peter Bossaerts, 2011. "Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-14, January.

    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:pone00:0311295. 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.