IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/67psy2016.html
   My bibliography  Save this paper

Asymmetric Modulation of Frn by the Probability and Value of Outcomes in Auditory Mid Task

Author

Listed:
  • Elena Krugliakova

    (National Research University Higher School of Economics)

  • Alexey Gorin

    (National Research University Higher School of Economics)

  • Anna Shestakova

    (National Research University Higher School of Economics)

  • Tommaso Fedele

    (National Research University Higher School of Economics)

  • Aleksandra Kuznetsova

    (National Research University Higher School of Economics)

  • Vasily Klucharev

    (National Research University Higher School of Economics)

Abstract

Reward prediction error (RPE) reflects the discrepancy between received and predicted outcomes and therefore plays an important role in learning in a dynamic environment. Using electroencephalographic measures of response to obtained and expected outcomes, previous studies have suggested that feedback-related negativity (FRN) could code RPE. It has further been hypothesized that FRN should be sensitive to both the likelihood and magnitude of behavioral outcomes. Previous studies consistently demonstrated that FRN is sensitive to the probability of outcomes, while the evidence of its sensitivity to the magnitude of outcomes is less consistent. In neuroimaging studies, a monetary incentive delay (MID) task is often used to evaluate the dependence of feedback processing on the RPE’s sign and size. In this article, for the first time, we studied FRN’s sensitivity to the valence, likelihood, and magnitude of outcomes during a novel auditory version of an MID task. FRN demonstrated sensitivity to both the valence of an outcome (gain vs. omission of a gain) and its probability (high vs. low). However, we did not observe a modulation of FRN amplitude by the magnitude of the outcomes. We also found that subjects’ behavior was more susceptible to changes in the probability than to the magnitude of the outcomes. Overall, FRN seems to be a promising tool to study the learning mechanisms of decision making

Suggested Citation

  • Elena Krugliakova & Alexey Gorin & Anna Shestakova & Tommaso Fedele & Aleksandra Kuznetsova & Vasily Klucharev, 2016. "Asymmetric Modulation of Frn by the Probability and Value of Outcomes in Auditory Mid Task," HSE Working papers WP BRP 67/PSY/2016, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:67psy2016
    as

    Download full text from publisher

    File URL: https://wp.hse.ru/data/2016/12/13/1111693205/67PSY2016_ek.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    2. Andrew Caplin & Mark Dean, 2008. "Dopamine, Reward Prediction Error, and Economics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(2), pages 663-701.
    3. Sagarnaga, Myriam & Ochoa, Rene F. & Salas, Jose M. & Anderson, David P. & Richardson, James W. & Knutson, Ronald D., 2000. "Mexican Representative Hog Farms 1995-2004 Economic Outlook: Preliminary Study," Research Reports 42786, Texas A&M University, Agricultural and Food Policy Center.
    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. Élise PAYZAN LE NESTOUR, 2010. "Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem," Swiss Finance Institute Research Paper Series 10-28, Swiss Finance Institute.
    2. Noah Gans & George Knox & Rachel Croson, 2007. "Simple Models of Discrete Choice and Their Performance in Bandit Experiments," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 383-408, December.
    3. Terry E. Daniel & Eyran J. Gisches & Amnon Rapoport, 2009. "Departure Times in Y-Shaped Traffic Networks with Multiple Bottlenecks," American Economic Review, American Economic Association, vol. 99(5), pages 2149-2176, December.
    4. Iftekhar, M. S. & Tisdell, J. G., 2018. "Learning in repeated multiple unit combinatorial auctions: An experimental study," Working Papers 267301, University of Western Australia, School of Agricultural and Resource Economics.
    5. Ianni, A., 2002. "Reinforcement learning and the power law of practice: some analytical results," Discussion Paper Series In Economics And Econometrics 203, Economics Division, School of Social Sciences, University of Southampton.
    6. Benaïm, Michel & Hofbauer, Josef & Hopkins, Ed, 2009. "Learning in games with unstable equilibria," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1694-1709, July.
    7. Oechssler, Jorg & Schipper, Burkhard, 2003. "Can you guess the game you are playing?," Games and Economic Behavior, Elsevier, vol. 43(1), pages 137-152, April.
    8. Erhao Xie, 2019. "Monetary Payoff and Utility Function in Adaptive Learning Models," Staff Working Papers 19-50, Bank of Canada.
    9. B Kelsey Jack, 2009. "Auctioning Conservation Contracts in Indonesia - Participant Learning in Multiple Trial Rounds," CID Working Papers 35, Center for International Development at Harvard University.
    10. Isabelle Brocas & Juan D. Carrillo, 2022. "The development of randomization and deceptive behavior in mixed strategy games," Quantitative Economics, Econometric Society, vol. 13(2), pages 825-862, May.
    11. James Choi & David Laibson & Brigitte Madrain & Andrew Metrick, 2007. "Reinforcement Learning in Investment Behavior," Levine's Bibliography 122247000000001737, UCLA Department of Economics.
    12. Brocas, Isabelle & Carrillo, Juan D., 2021. "Value computation and modulation: A neuroeconomic theory of self-control as constrained optimization," Journal of Economic Theory, Elsevier, vol. 198(C).
    13. Enkhtaivan, Bolortuya & Davaadorj, Zagdbazar, 2021. "Do they recall their past? CEOs’ liquidity policies across firms as they switch jobs," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    14. Anthony Ziegelmeyer & Frédéric Koessler & Kene Boun My & Laurent Denant-Boèmont, 2008. "Road Traffic Congestion and Public Information: An Experimental Investigation," Journal of Transport Economics and Policy, University of Bath, vol. 42(1), pages 43-82, January.
    15. DeJong, D.V. & Blume, A. & Neumann, G., 1998. "Learning in Sender-Receiver Games," Other publications TiSEM 4a8b4f46-f30b-4ad2-bb0c-1, Tilburg University, School of Economics and Management.
    16. Sergiu Hart & Andreu Mas-Colell, 2013. "A Simple Adaptive Procedure Leading To Correlated Equilibrium," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 2, pages 17-46, World Scientific Publishing Co. Pte. Ltd..
    17. Marco LiCalzi & Roland Mühlenbernd, 2022. "Feature-weighted categorized play across symmetric games," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1052-1078, June.
    18. Ferraro Paul J & Vossler Christian A, 2010. "The Source and Significance of Confusion in Public Goods Experiments," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-42, July.
    19. Mariano Runco, 2013. "Estimating depth of reasoning in a repeated guessing game with no feedback," Experimental Economics, Springer;Economic Science Association, vol. 16(3), pages 402-413, September.
    20. Fernando Lozano & Jaime Lozano & Mario García, 2007. "An artificial economy based on reinforcement learning and agent based modeling," Documentos de Trabajo 3907, Universidad del Rosario.

    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:hig:wpaper:67psy2016. 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: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

    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.