Fast-backward replay of sequentially memorized items in humans

  1. Qiaoli Huang  Is a corresponding author
  2. Jianrong Jia
  3. Qiming Han
  4. Huan Luo  Is a corresponding author
  1. Peking University, China

Abstract

Storing temporal sequences of events (i.e., sequence memory) is fundamental to many cognitive functions. However, how the sequence order information is maintained and represented in working memory and its behavioral significance, particularly in human subjects, remains unknown. Here, we recorded electroencephalography (EEG) in combination with a temporal response function (TRF) method to dissociate item-specific neuronal reactivations. We demonstrate that serially remembered items are successively reactivated during memory retention. The sequential replay displays two interesting properties compared to the actual sequence. First, the item-by-item reactivation is compressed within a 200-400 ms window, suggesting that external events are associated within a plasticity-relevant window to facilitate memory consolidation. Second, the replay is in a temporally reversed order and is strongly related to the recency effect in behavior. This fast-backward replay, previously revealed in rat hippocampus and demonstrated here in human cortical activities, might constitute a general neural mechanism for sequence memory and learning.

Data availability

All data generated during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. Qiaoli Huang

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    For correspondence
    qiaolihuang@pku.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
  2. Jianrong Jia

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7665-1182
  3. Qiming Han

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Huan Luo

    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    For correspondence
    huan.luo@pku.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8349-9796

Funding

National Natural Science Foundation of China (31522027)

  • Huan Luo

National Natural Science Foundation of China (31571115)

  • Huan Luo

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

Ethics

Human subjects: All participants provided written informed consent prior to the start of the experiment, which was approved by the Research Ethics Committee at Peking University (2015-03-05c2).

Copyright

© 2018, Huang 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

  • 4,603
    views
  • 770
    downloads
  • 38
    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. Qiaoli Huang
  2. Jianrong Jia
  3. Qiming Han
  4. Huan Luo
(2018)
Fast-backward replay of sequentially memorized items in humans
eLife 7:e35164.
https://doi.org/10.7554/eLife.35164

Share this article

https://doi.org/10.7554/eLife.35164

Further reading

    1. Neuroscience
    Maëliss Jallais, Marco Palombo
    Research Article

    This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.

    1. Neuroscience
    Bharath Krishnan, Noah Cowan
    Insight

    Mice can generate a cognitive map of an environment based on self-motion signals when there is a fixed association between their starting point and the location of their goal.