Journal article
Post-encoding reactivation is related to learning of episodes in humans
- Abstract:
- Prior animal and human studies have shown that post-encoding reinstatement plays an important role in organizing the temporal sequence of unfolding episodes in memory. Here, we investigated whether post-encoding reinstatement serves to promote the encoding of "one-shot" episodic learning beyond the temporal structure in humans. In Experiment 1, participants encoded sequences of pictures depicting unique and meaningful episodic-like events. We used representational similarity analysis on scalp EEG recordings during encoding and found evidence of rapid picture-elicited EEG pattern reinstatement at episodic offset (around 500 msec post-episode). Memory reinstatement was not observed between successive elements within an episode, and the degree of memory reinstatement at episodic offset predicted later recall for that episode. In Experiment 2, participants encoded a shuffled version of the picture sequences from Experiment 1, rendering each episode meaningless to the participant but temporally structured as in Experiment 1, and we found no evidence of memory reinstatement at episodic offset. These results suggest that post-encoding memory reinstatement is akin to the rapid formation of unique and meaningful episodes that unfold over time.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.2MB, Terms of use)
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- Publisher copy:
- 10.1162/jocn_a_01934
Authors
- Publisher:
- MIT Press
- Journal:
- Journal of Cognitive Neuroscience More from this journal
- Volume:
- 35
- Issue:
- 1
- Pages:
- 74-89
- Publication date:
- 2022-12-01
- Acceptance date:
- 2022-10-08
- DOI:
- EISSN:
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1530-8898
- ISSN:
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0898-929X
- Pmid:
-
36306242
- Language:
-
English
- Keywords:
- Pubs id:
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1311159
- Local pid:
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pubs:1311159
- Deposit date:
-
2023-06-20
- ARK identifier:
Terms of use
- Copyright holder:
- Massachusetts Institute of Technology
- Copyright date:
- 2022
- Rights statement:
- © 2022 Massachusetts Institute of Technology
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