Evidence map›Paper›PMID 38961049›Full record

ArticleMemory & cognition2025

Effect of levels-of-processing on rates of forgetting.

Nan Peng, Robert H Logie, Sergio Della Sala

Abstract read
In one paragraph

Article in Memory & cognition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 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

3 authors.

Nan PengHuman Cognitive Neuroscience, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK. nan.peng@ed.ac.uk.ORCID 0000-0002-9492-7418
Robert H LogieHuman Cognitive Neuroscience, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
Sergio Della SalaHuman Cognitive Neuroscience, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK. sergio@ed.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The levels-of-processing (LOP) framework, proposing that deep processing yields superior retention, has provided an important paradigm for memory research and a practical means of improving learning. However, the available levels-of-processing literature focuses on immediate memory performance. It is assumed within the LOP framework that deep processing will lead to slower forgetting than will shallow processing. However, it is unclear whether, or how, the initial level of processing affects the forgetting slopes over longer retention intervals. The present three experiments were designed to explore whether items encoded at qualitatively different LOP are forgotten at different rates. In the first two experiments, depth of processing was manipulated within-participants at encoding under deep and shallow conditions (semantic vs. rhyme judgement in Experiment 1; semantic vs. consonant-vowel pattern decision in Experiment 2). Recognition accuracy (d prime) was measured between-participants immediately after learning and at 30-min, 2-h, and 24-h delays. The third experiment employed a between-participants design, contrasting the rates of forgetting following semantic and phonological (rhyme) processing at immediate, 30-min, 2-h, and 6-h delays. Results from the three experiments consistently demonstrated a large effect size of levels of processing on immediate performance and a medium-to-large level effect size on delayed recognition, but crucially no LOP × delay group interaction. Analysis of the retention curves revealed no significant differences between the slopes of forgetting for deep and shallow processing. These results suggest that the rates of forgetting are independent of the qualitatively distinct encoding operations manipulated by levels of processing.

Indexed as

Mental RecallRecognition, PsychologyRetention, PsychologyAdultFemaleHumansMaleSemanticsYoung AdultEncodingEpisodic memoryLevels of processingLong-term forgettingRecognition

Identifiers

PMID38961049
PMCPMC11868305

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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.