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The computational nature of memory modification

  1. Samuel J Gershman  Is a corresponding author
  2. Marie-H Monfils
  3. Kenneth A Norman
  4. Yael Niv
  1. Harvard University, United States
  2. University of Texas at Austin, United States
  3. Princeton University, United States
Research Article
  • Cited 42
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Cite this article as: eLife 2017;6:e23763 doi: 10.7554/eLife.23763

Abstract

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.

Article and author information

Author details

  1. Samuel J Gershman

    Department of Psychology, Harvard University, Cambridge, United States
    For correspondence
    gershman@fas.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6546-3298
  2. Marie-H Monfils

    Department of Psychology, University of Texas at Austin, Austin, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Kenneth A Norman

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Yael Niv

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0259-8371

Funding

National Science Foundation (Graduate research fellowship)

  • Samuel J Gershman

Sloan Research Foundation (Sloan Research Fellowship)

  • Yael Niv

National Institutes of Health (R01MH091147)

  • Marie-H Monfils

National Institutes of Health (R21MH086805)

  • Marie-H Monfils

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Michael J Frank, Brown University, United States

Publication history

  1. Received: December 10, 2016
  2. Accepted: March 13, 2017
  3. Accepted Manuscript published: March 15, 2017 (version 1)
  4. Accepted Manuscript updated: March 16, 2017 (version 2)
  5. Version of Record published: April 13, 2017 (version 3)
  6. Version of Record updated: May 22, 2017 (version 4)

Copyright

© 2017, Gershman et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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