IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1013143.html
   My bibliography  Save this article

Whom do we prefer to learn from in observational reinforcement learning?

Author

Listed:
  • Gota Morishita
  • Carsten Murawski
  • Nitin Yadav
  • Shinsuke Suzuki

Abstract

Learning by observing others’ experiences is a hallmark of human intelligence. While the neurocomputational mechanisms underlying observational learning are well understood, less is known about whom people prefer to learn from in the context of observational learning. One hypothesis posits that learners prefer individuals who exhibit a high degree of decision noise, ‘free riding’ on the costly exploration of others. An alternative hypothesis is that learners prefer individuals with low decision noise, as lower decision noise is often associated with better performance. In a preregistered experiment, we found that most participants preferred to learn from low-noise (high-performing) individuals. Furthermore, exploratory analyses revealed that participants who preferred low-noise individuals tended to rely on imitation of others’ actions. These findings offer a potential computational account of how learning styles are related to partner selection in social learning.Author summary: In our daily lives, we often learn by watching others. For example, when starting a new job, we might watch an experienced colleague to learn effective strategies, or an inexperienced coworker to avoid common mistakes. While previous studies have examined how people learn by observing others, less is known about how we decide whom to observe. This study explored whether people prefer to learn from individuals who make consistent choices or those who behave more randomly. At first glance, the answer seems obvious: we would naturally prefer to learn from those who make consistent, reliable decisions. However, there can also be value in learning from someone who behaves unpredictably. For example, when searching for a good restaurant, observing an adventurous friend who tries unfamiliar places might help us discover hidden gems. Despite this potential advantage, we found that most participants preferred consistent decision-makers. Further analysis revealed that participants who favored reliable partners tended to imitate their actions. Our findings suggest that personal learning styles shape partner preferences. These insights could help us understand how people choose whom to learn from in everyday settings like classrooms, or workplaces.

Suggested Citation

  • Gota Morishita & Carsten Murawski & Nitin Yadav & Shinsuke Suzuki, 2025. "Whom do we prefer to learn from in observational reinforcement learning?," PLOS Computational Biology, Public Library of Science, vol. 21(12), pages 1-21, December.
  • Handle: RePEc:plo:pcbi00:1013143
    DOI: 10.1371/journal.pcbi.1013143
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013143
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1013143&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1013143?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
    ---><---

    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:pcbi00:1013143. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.