IDEAS home Printed from https://ideas.repec.org/a/nat/nathum/v3y2019i9d10.1038_s41562-019-0637-z.html
   My bibliography  Save this article

Neurocomputational mechanisms underlying motivated seeing

Author

Listed:
  • Yuan Chang Leong

    (Stanford University)

  • Brent L. Hughes

    (University of California)

  • Yiyu Wang

    (Northeastern University)

  • Jamil Zaki

    (Stanford University)

Abstract

People tend to believe that their perceptions are veridical representations of the world, but also commonly report perceiving what they want to see or hear. It remains unclear whether this reflects an actual change in what people perceive or merely a bias in their responding. Here we manipulated the percept that participants wanted to see as they performed a visual categorization task. Even though the reward-maximizing strategy was to perform the task accurately, the manipulation biased participants’ perceptual judgements. Motivation increased neural activity selective for the motivationally relevant category, indicating a bias in participants’ neural representation of the presented image. Using a drift diffusion model, we decomposed motivated seeing into response and perceptual components. Response bias was associated with anticipatory activity in the nucleus accumbens, whereas perceptual bias tracked category-selective neural activity. Our results provide a computational description of how the drive for reward leads to inaccurate representations of the world.

Suggested Citation

  • Yuan Chang Leong & Brent L. Hughes & Yiyu Wang & Jamil Zaki, 2019. "Neurocomputational mechanisms underlying motivated seeing," Nature Human Behaviour, Nature, vol. 3(9), pages 962-973, September.
  • Handle: RePEc:nat:nathum:v:3:y:2019:i:9:d:10.1038_s41562-019-0637-z
    DOI: 10.1038/s41562-019-0637-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41562-019-0637-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41562-019-0637-z?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Xumin & Khachatryan, Hayk & Gao, Zhifeng, 2020. "Using Mixed Logit Based Models to Control Attribute Nonattendance in Choice Experiments," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304547, Agricultural and Applied Economics Association.
    2. Fatma Trabelsi & Salsebil Bel Hadj Ali, 2022. "Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia," Sustainability, MDPI, vol. 14(4), pages 1-23, February.

    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:nathum:v:3:y:2019:i:9:d:10.1038_s41562-019-0637-z. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.