IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-46921-5.html
   My bibliography  Save this article

Timing along the cardiac cycle modulates neural signals of reward-based learning

Author

Listed:
  • Elsa F. Fouragnan

    (University of Oxford
    University of Plymouth
    University of Plymouth)

  • Billy Hosking

    (University of Plymouth
    University of Plymouth)

  • Yin Cheung

    (University of Oxford)

  • Brooke Prakash

    (University of Oxford)

  • Matthew Rushworth

    (University of Oxford)

  • Alejandra Sel

    (University of Oxford
    University of Essex
    University of Essex)

Abstract

Natural fluctuations in cardiac activity modulate brain activity associated with sensory stimuli, as well as perceptual decisions about low magnitude, near-threshold stimuli. However, little is known about the relationship between fluctuations in heart activity and other internal representations. Here we investigate whether the cardiac cycle relates to learning-related internal representations – absolute and signed prediction errors. We combined machine learning techniques with electroencephalography with both simple, direct indices of task performance and computational model-derived indices of learning. Our results demonstrate that just as people are more sensitive to low magnitude, near-threshold sensory stimuli in certain cardiac phases, so are they more sensitive to low magnitude absolute prediction errors in the same cycles. However, this occurs even when the low magnitude prediction errors are associated with clearly suprathreshold sensory events. In addition, participants exhibiting stronger differences in their prediction error representations between cardiac cycles exhibited higher learning rates and greater task accuracy.

Suggested Citation

  • Elsa F. Fouragnan & Billy Hosking & Yin Cheung & Brooke Prakash & Matthew Rushworth & Alejandra Sel, 2024. "Timing along the cardiac cycle modulates neural signals of reward-based learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46921-5
    DOI: 10.1038/s41467-024-46921-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-46921-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-46921-5?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. Marco K. Wittmann & Elsa Fouragnan & Davide Folloni & Miriam C. Klein-Flügge & Bolton K. H. Chau & Mehdi Khamassi & Matthew F. S. Rushworth, 2020. "Global reward state affects learning and activity in raphe nucleus and anterior insula in monkeys," Nature Communications, Nature, vol. 11(1), pages 1-17, December.
    2. Elsa Fouragnan & Chris Retzler & Karen Mullinger & Marios G. Philiastides, 2015. "Two spatiotemporally distinct value systems shape reward-based learning in the human brain," Nature Communications, Nature, vol. 6(1), pages 1-11, November.
    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. Patricia L. Lockwood & Jo Cutler & Daniel Drew & Ayat Abdurahman & Deva Sanjeeva Jeyaretna & Matthew A. J. Apps & Masud Husain & Sanjay G. Manohar, 2024. "Human ventromedial prefrontal cortex is necessary for prosocial motivation," Nature Human Behaviour, Nature, vol. 8(7), pages 1403-1416, July.
    2. Ethan Trepka & Mehran Spitmaan & Bilal A. Bari & Vincent D. Costa & Jeremiah Y. Cohen & Alireza Soltani, 2021. "Entropy-based metrics for predicting choice behavior based on local response to reward," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    3. M. A. Pisauro & E. F. Fouragnan & D. H. Arabadzhiyska & M. A. J. Apps & M. G. Philiastides, 2022. "Neural implementation of computational mechanisms underlying the continuous trade-off between cooperation and competition," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    4. Chen Qu & Elise Météreau & Luigi Butera & Marie Claire Villeval & Jean-Claude Dreher & Matthew Rushworth, 2019. "Neurocomputational mechanisms at play when weighing concerns for extrinsic rewards, moral values, and social image," Post-Print halshs-02193425, HAL.
    5. Tarryn Balsdon & M. Andrea Pisauro & Marios G. Philiastides, 2024. "Distinct basal ganglia contributions to learning from implicit and explicit value signals in perceptual decision-making," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Colin W. Hoy & David R. Quiroga-Martinez & Eduardo Sandoval & David King-Stephens & Kenneth D. Laxer & Peter Weber & Jack J. Lin & Robert T. Knight, 2023. "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Johannes Algermissen & Jennifer C. Swart & René Scheeringa & Roshan Cools & Hanneke E. M. den Ouden, 2024. "Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    8. Ruth Pauli & Inti A. Brazil & Gregor Kohls & Miriam C. Klein-Flügge & Jack C. Rogers & Dimitris Dikeos & Roberta Dochnal & Graeme Fairchild & Aranzazu Fernández-Rivas & Beate Herpertz-Dahlmann & Amaia, 2023. "Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    9. A. Calapai & J. Cabrera-Moreno & T. Moser & M. Jeschke, 2022. "Flexible auditory training, psychophysics, and enrichment of common marmosets with an automated, touchscreen-based system," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

    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:15:y:2024:i:1:d:10.1038_s41467-024-46921-5. 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.