Evidence map›Paper›PMID 35106538›Full record

ArticleCerebral cortex (New York, N.Y. : 1991)2022

Encoding contexts are incidentally reinstated during competitive retrieval and track the temporal dynamics of memory interference.

Inês Bramão, Jiefeng Jiang, Anthony D Wagner, Mikael Johansson

Abstract read
In one paragraph

Article in Cerebral cortex (New York, N.Y. : 1991), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Maintenance suppression enhances subsequent associative learning.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Inês BramãoDepartment of Psychology, Lund University, Lund SE-221 00, Sweden.ORCID 0000-0003-2635-2381
Jiefeng JiangDepartment of Psychological and Brain Sciences, University of Iowa, Iowa 52242-1407, USA.ORCID 0000-0002-4264-6382
Anthony D WagnerDepartment of Psychology, Stanford University, CA 94305, USA.ORCID 0000-0003-0624-4543
Mikael JohanssonDepartment of Psychology, Lund University, Lund SE-221 00, Sweden.ORCID 0000-0002-2903-6301

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to remember an episode from our past is often hindered by competition from similar events. For example, if we want to remember the article a colleague recommended during the last lab meeting, we may need to resolve interference from other article recommendations from the same colleague. This study investigates if the contextual features specifying the encoding episodes are incidentally reinstated during competitive memory retrieval. Competition between memories was created through the AB/AC interference paradigm. Individual word-pairs were presented embedded in a slowly drifting real-word-like context. Multivariate pattern analysis (MVPA) of high temporal-resolution electroencephalographic (EEG) data was used to investigate context reactivation during memory retrieval. Behaviorally, we observed proactive (but not retroactive) interference; that is, performance for AC competitive retrieval was worse compared with a control DE noncompetitive retrieval, whereas AB retrieval did not suffer from competition. Neurally, proactive interference was accompanied by an early reinstatement of the competitor context and interference resolution was associated with the ensuing reinstatement of the target context. Together, these findings provide novel evidence showing that the encoding contexts of competing discrete events are incidentally reinstated during competitive retrieval and that such reinstatement tracks retrieval competition and subsequent interference resolution.

Indexed as

Memory, EpisodicElectroencephalographyMental Recallelectroencephalography (EEG)episodic memorymemory reactivationmultivariate-pattern analysis (MVPA)

Identifiers

PMID35106538
PMCPMC9667177

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.