IDEAS home Printed from https://ideas.repec.org/a/nat/nathum/v5y2021i7d10.1038_s41562-021-01051-6.html
   My bibliography  Save this article

Geometric models reveal behavioural and neural signatures of transforming experiences into memories

Author

Listed:
  • Andrew C. Heusser

    (Dartmouth College
    Akili Interactive Labs)

  • Paxton C. Fitzpatrick

    (Dartmouth College)

  • Jeremy R. Manning

    (Dartmouth College)

Abstract

How do we preserve and distort our ongoing experiences when encoding them into episodic memories? The mental contexts in which we interpret experiences are often person-specific, even when the experiences themselves are shared. Here we develop a geometric framework for mathematically characterizing the subjective conceptual content of dynamic naturalistic experiences. We model experiences and memories as trajectories through word-embedding spaces whose coordinates reflect the universe of thoughts under consideration. Memory encoding can then be modelled as geometrically preserving or distorting the ‘shape’ of the original experience. We applied our approach to data collected as participants watched and verbally recounted a television episode while undergoing functional neuroimaging. Participants’ recountings preserved coarse spatial properties (essential narrative elements) but not fine spatial scale (low-level) details of the episode’s trajectory. We also identified networks of brain structures sensitive to these trajectory shapes.

Suggested Citation

  • Andrew C. Heusser & Paxton C. Fitzpatrick & Jeremy R. Manning, 2021. "Geometric models reveal behavioural and neural signatures of transforming experiences into memories," Nature Human Behaviour, Nature, vol. 5(7), pages 905-919, July.
  • Handle: RePEc:nat:nathum:v:5:y:2021:i:7:d:10.1038_s41562-021-01051-6
    DOI: 10.1038/s41562-021-01051-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41562-021-01051-6
    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/s41562-021-01051-6?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. Xinming Xu & Ziyan Zhu & Xueyao Zheng & Jeremy R. Manning, 2024. "Temporal asymmetries in inferring unobserved past and future events," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Hongmi Lee & Janice Chen, 2022. "Predicting memory from the network structure of naturalistic events," Nature Communications, Nature, vol. 13(1), pages 1-14, 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:nathum:v:5:y:2021:i:7:d:10.1038_s41562-021-01051-6. 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.