Evidence map›Paper›PMID 38969954›Full record

ArticleMemory & cognition2024

Active maintenance in working memory reinforces bindings for future retrieval from episodic long-term memory.

Vanessa M Loaiza, Alessandra S Souza

Abstract read
In one paragraph

Article in Memory & cognition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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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

2 authors.

Vanessa M LoaizaDepartment of Psychology, University of Sheffield, 219 Portobello, Sheffield, S1 4DP, UK. v.loaiza@sheffield.ac.uk.ORCID 0000-0002-5000-7089
Alessandra S SouzaFaculty of Psychology and Education Sciences, Center for Psychology, University of Porto, Porto, Portugal.ORCID 0000-0002-1057-8426

Funding

Center for Psychology of the University of Porto FCT/UIDB/00050/2020Portuguese Foundation for Science and Technology UIDB/00050/2020Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 100014_192204
6 · The paper itself

Abstract

Many theories assume that actively maintaining information in working memory (WM) predicts its retention in episodic long-term memory (LTM), as revealed by the beneficial effects of more WM time. In four experiments, we examined whether affording more time for intentional WM maintenance does indeed drive LTM. Sequences of four words were presented during trials of simple span (short time), slow span (long time), and complex span (long time with distraction; Experiments 1-2). Long time intervals entailed a pause of equivalent duration between the words that presented a blank screen (slow span) or an arithmetic problem to read aloud and solve (complex span). In Experiments 1-3, participants either serially recalled the words (intentional encoding) or completed a no-recall task (incidental encoding). In Experiment 4, all participants were instructed to intentionally encode the words, with the trials randomly ending in the serial-recall or no-recall task. To ensure similar processing of the words between encoding groups, participants silently decided whether each word was a living or nonliving thing via key press (i.e., an animacy judgment; Experiments 1 and 3-4) or read the words aloud and then pressed the space bar (Experiment 2). A surprise delayed memory test at the end of the experiment assessed LTM. Applying Bayesian cognitive models to disambiguate binding and item memory revealed consistent benefits of free time to binding memory that were specific to intentional encoding in WM. This suggests that time spent intentionally keeping information in WM is special for LTM because WM is a system that maintains bindings.

Indexed as

Memory, EpisodicMemory, Long-TermMemory, Short-TermMental RecallAdultFemaleHumansMaleYoung AdultCognitive modelingEpisodic memoryLong-term memoryWorking memory

Identifiers

PMID38969954
PMCPMC11588845

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Registered trials

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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.