1. Neuroscience

The computational nature of memory modification

  1. Samuel J Gershman  Is a corresponding author
  2. Marie-H Monfils
  3. Norman A Kenneth
  4. Yael Niv
  1. Harvard University, United States
  2. University of Texas at Austin, United States
  3. Princeton University, United States
https://doi.org/10.7554/eLife.23763
Share this article
Cite this article
  1. Samuel J Gershman
  2. Marie-H Monfils
  3. Norman A Kenneth
  4. Yael Niv
(2017)
The computational nature of memory modification
eLife 6:e23763.
https://doi.org/10.7554/eLife.23763
  2. Download BibTeX
  3. Download .RIS

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. Norman A Kenneth

    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.

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.

Metrics

  • 7,871
    views
  • 1,491
    downloads
  • 119
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Samuel J Gershman
  2. Marie-H Monfils
  3. Norman A Kenneth
  4. Yael Niv
(2017)
The computational nature of memory modification
eLife 6:e23763.
https://doi.org/10.7554/eLife.23763

Share this article

https://doi.org/10.7554/eLife.23763

Categories and tags

Further reading

    1. Neuroscience

    Robust variability of grid cell properties within individual grid modules enhances encoding of local space

    William T Redman, Santiago Acosta-Mendoza ... Michael J Goard
    Research Article

    Although grid cells are one of the most well-studied functional classes of neurons in the mammalian brain, whether there is a single orientation and spacing value per grid module has not been carefully tested. We analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the encoding of local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that grid property variability of a similar magnitude to the analyzed data leads to significantly decreased decoding error. This holds even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

    1. Neuroscience

    Reduced discrimination between signals of danger and safety but not overgeneralization is linked to exposure to childhood adversity in healthy adults

    Maren Klingelhöfer-Jens, Katharina Hutterer ... Tina B Lonsdorf
    Research Article

    Childhood adversity is a strong predictor of developing psychopathological conditions. Multiple theories on the mechanisms underlying this association have been suggested which, however, differ in the operationalization of ‘exposure.’ Altered (threat) learning mechanisms represent central mechanisms by which environmental inputs shape emotional and cognitive processes and ultimately behavior. 1402 healthy participants underwent a fear conditioning paradigm (acquisition training, generalization), while acquiring skin conductance responses (SCRs) and ratings (arousal, valence, and contingency). Childhood adversity was operationalized as (1) dichotomization, and following (2) the specificity model, (3) the cumulative risk model, and (4) the dimensional model. Individuals exposed to childhood adversity showed blunted physiological reactivity in SCRs, but not ratings, and reduced CS+/CS- discrimination during both phases, mainly driven by attenuated CS+ responding. The latter was evident across different operationalizations of ‘exposure’ following the different theories. None of the theories tested showed clear explanatory superiority. Notably, a remarkably different pattern of increased responding to the CS- is reported in the literature for anxiety patients, suggesting that individuals exposed to childhood adversity may represent a specific sub-sample. We highlight that theories linking childhood adversity to (vulnerability to) psychopathology need refinement.